As per a Gartner report, over 30% of GenAI projects will be dropped by 2026 due to poor data quality, high costs or unclear business value.

Your 4-step blueprint for GenAI success

1. Assess your current data capabilities and needs

Use a data & analytics maturity model to understand your organization’s maturity level – this stock taking is crucial to the success of your data strategy.

• Are you at an aspiration stage, where you will focus on building a strong data foundation and basic analytics capabilities?
• Or have you progressed to having the capability, where you can prioritize building a data science team and advanced analytics skills?
• Or have your team reached a point of competency, where you are ready to start exploring GenAI applications?

A good data and analytics strategy starts with a clear vision. Gartner

In addition to your organization’s maturity level, evaluate your team’s skills and knowledge in AI and machine learning. Identify any skill gaps that need to be addressed through training or hiring. Consider the availability of specialized AI talent within your organization or the potential to partner with external experts. By understanding your team’s capabilities, you can create a roadmap for skill development, allowing you to focus on immediate needs while planning for future expansion plans.

2. Build a robust modern data foundation

One of the first steps in harnessing GenAI is to break down data silos within your organization. Often, crucial data is scattered across multiple platforms, making it difficult to get a unified view. Collecting, integrating and processing this data into a single, cohesive platform is essential for effective AI application.

Think of a retailer with customer data locked in outdated systems. By consolidating this data, they can unlock powerful insights and deliver personalized customer experiences.

Once integrated, it’s vital to ensure the data is clean, well-governed and secure. Poor data quality can lead to flawed AI outputs, while weak governance and security can expose your organization to risks. Building a modern data foundation with these principles in mind will lay the groundwork for reliable and impactful AI applications.

3. Make the right technology and model choices for your GenAI & data needs

Choosing the right technology and AI models is crucial for project success. Consider factors like your team’s expertise, available data and cost. If your team is comfortable with open-source models like Hugging Face or GPT, they offer flexibility but require more maintenance. Managed models like Amazon Bedrock or SageMaker are easier to implement with less upkeep but offer less customization.

Balancing these factors is key to a successful AI implementation. Opt for solutions that your team can manage effectively, that leverage your available data, and that fit within your budget. A well-thought-out choice here can save you time and money in the long run while ensuring robust performance.

Remember, AI performance is not just about speed and processing power; it is about delivering these results without ballooning costs. Proprietary systems can inflate total cost of ownership (TCO) with specialized hardware, licensing fees, and other hidden costs that make scaling more expensive in the long term.

One of the common misconceptions is that all AI workloads must run on expensive GPUs, which, while powerful, are not always the most efficient or necessary option for every type of task.

When selecting a model, evaluate accuracy, cost, latency and scalability. Balancing these factors ensures the chosen solution aligns with your team’s capabilities, available resources, and long-term goals.

The AI universe is constantly evolving, and needs solutions that are not just designed for today, but for tomorrow. Proprietary systems often lock clients into specific technologies, making it costly and difficult to upgrade as AI capabilities advance. Look for open, future-proof AI systems that adapt to new technologies and workloads. This long-term flexibility gives you the confidence that their AI investments will remain viable, no matter how your needs evolve.

4. Use tools and accelerators to advance your AI & GenAI dev lifecycle

Don’t reinvent the wheel – use available cutting-edge tools and platforms to accelerate your GenAI projects. The goal is to streamline the process from data collection to actionable insights. Utilizing accelerators and pre-built tools can help you quickly move from concept to deployment, ensuring that your AI applications deliver value rapidly and efficiently.

If you are working on refining large language models (LLMs), platforms that offer distributed training and fine-tuning can significantly reduce the time from data to insights. These tools not only speed up development but also ensure that your AI models are optimized for performance and cost.

• To optimize AI pipelines, organizations can turn, for example, to the latest Intel Xeon processors with Intel Advanced Matrix Extensions (Intel AMX), a built-in AI accelerator. As part of Intel® AI Engines, Intel AMX was designed to balance inference, the most prominent use case for a CPU in AI applications, with more capabilities for training.
• To optimize Retrieval-Augmented Generation (RAG) pipelines, Langchain is a powerful tool for orchestrating workflows across different data sources. It connects language models with databases, APIs, and cloud storage, enabling seamless chaining of tasks like data retrieval and prompt transformation. This flexibility makes it easy to build scalable RAG systems that can quickly process and augment responses with relevant information.
• For managing embeddings, Amazon Bedrock Knowledgebase can offer a managed vector store solution that integrates with various embedding models. It provides scalable, efficient storage for embeddings, allowing fast retrieval of contextually relevant information from multiple data sources.
• By combining Langchain for orchestration and Bedrock for vector storage, you can build a highly efficient, adaptable RAG pipeline that accelerates insights and enhances AI applications.

By following these steps, you will be well on your way to implementing GenAI in a way that is both strategic and sustainable, driving meaningful outcomes for your organization.

The post Defining Your GenAI Data Strategy: A Roadmap for Enterprise Data Teams appeared first on Cloud Kinetics.

Every client and customer cares deeply about security. Regardless of the domain, industry or specific application, when a workload is moved to or created in AWS, security and data protection are always important components of the architectural design. To meet these critical security requirements, organizations use AWS Control Tower and Landing Zones, which enable a secure and compliant foundation for your AWS environment.

Setting up an AWS Control Tower and Landing Zone

Setting up a Control Tower and Landing Zone for your enterprise applications can help mitigate many security risks and provide a consolidated and comprehensive view of your AWS landscape. AWS Control Tower and Landing Zone thoroughly propagate the concept of “define once and use across”. This enables you to set up a well-architected, multi-account environment in hours instead of weeks or months, utilizing best practice blueprints tailored to the organization’s needs.

As a security best practice, it is always recommended to have a multi-account approach, where accounts can be categorized based on application/environment/business-unit etc. This approach enhances security through segregation and isolation of workloads and data. This will also significantly reduce the surface attack area when there is a security compromise.

As your organization and customer needs grow, you will have a bunch of accounts to handle – that is when the Control Tower and Landing Zone comes to the rescue as it provides centralized control and policy management across the accounts.

Figure 1 here depicts how you can have all AWS accounts under AWS Organizations and tap on Control Tower features to enforce security and governance.

Setting up a Landing Zone is always one of the best approaches for customer applications running on multiple AWS accounts, if you want to ensure strict traffic inspection, central security logging, and cost savings.

Building a Landing Zone with AWS Control Tower: Core concepts

Here are the core concepts at play while establishing a robust AWS Control Tower and Landing Zone foundational implementation.

Organizational Units: When it comes to planning Organizational Units (OUs), there is no one-size-fits-all approach. It should be based on the best way you can categorize your workloads.

For example, an enterprise company with numerous applications, business units and multiple environments (e.g. Dev/UAT/Prod) can be structured as shown below.

Service Control Policies/Guardrails: Service Control Policies (SCPs) are effectively a policy-driven preventive guardrail. AWS SCPs/guardrails provide organizations with robust tools for optimizing and centrally managing governance, security & compliance enforcement, permission management and operational efficiency. These features make them critical components of a well-architected multi-account strategy in AWS. We recommend you enable all the required guardrails as per industry standards. To get started with AWS Best Practice guardrails, you can refer to the official AWS guidance documentation found here.

You could also create a Test OU within which you can enable and test the effect of guardrails without impacting any production workloads. This is prudent when teams may be unaware of how an SCP guardrail may impact your team’s day to day activities.

Networking for Landing Zone: This is the most important part which often consumes a lot of time for planning. We recommend that organizations should plan for three different types of traffic flow:

• Ingress traffic: The Landing Zone should have a highly available firewall for ingress traffic inspection. Organizations can choose from a number of firewall appliances in the market today, including native AWS Network Firewall.
• Egress traffic: Plan for a central NAT gateway to manage all the egress traffic originating from workload VPCs from all AWS accounts and ensure it is being inspected using the firewall. This saves cost when all your workload VPCs use a central NAT gateway instead of individual NAT gateways for VPCs and enhances security through comprehensive monitoring with a holistic view.
• Inter-VPC traffic: Also referred to as East-West traffic, this is internal traffic between the VPCs within the AWS accounts. Best practice is to have inter-VPC traffic inspected using a firewall to detect internal threats, prevent data exfiltration etc. However, you can choose to ignore inspection for some VPCs – for example, those which require high throughput and require less latency.

We recommend you further define any other flows for your workloads and analyse its effects including latency, costs when it traverses transit gateway, or other cost incurring components.

Figure 3 here shows one of the ways you can design your architecture to achieve the above-mentioned traffic inspection and flows. It uses hub and spoke architecture using AWS transit gateway.

To read more about hub and spoke architecture refer to this AWS article here.

Logging and monitoring: Logs can be considered as a critical asset in any infrastructure. There are a number of different kinds like VPC flow logs, firewall logs, DNS logs, transit gateway attachment logs, application logs, and more. AWS Control Tower offers you a way to centrally store these logs in a separate “Log Archive” account. While AWS Control Tower has the capability to implement centralized storage, the responsibility of log ingestion implementation is allocated to the organization. The logs can be further ingested to other Security Information and Event Management (SIEM) platforms for analytics.

Putting things in motion

By continuously monitoring workload performance and analysing customer feedback, organizations can effectively adjust resources and processes to align with evolving customer needs, ensuring enhanced service delivery and operational efficiency.

AWS offers native service called AWS CloudWatch, to get you started with Logging and Monitoring – refer to this official AWS documentation. This should help you understand what AWS services’ logs can be centralized, among other important notes.

To sum up, using the AWS Control Tower service will unveil a holistic view of security and governance across all AWS accounts within the organization. Setting up a Landing Zone makes it easier to have total control on all of the traffic entering and exiting your AWS accounts. This will significantly reduce the surface attack area and can efficiently create a scalable architecture and ensure security best practices.

For additional Design Consultation and Cloud Engineering support, Cloud Kinetics currently offers a refined AWS Secure Landing Zone (SLZ) implementation, available here via AWS Marketplace. You can also get in touch with us directly here.

The post AWS Control Tower and Landing Zone: Architecture & Best Practices appeared first on Cloud Kinetics.

According to a McKinsey evaluation, the key to GenAI success is in identifying practical applications that drive real business value rather than getting carried away by exaggerated claims.

Generative AI refers to AI systems that create new content, such as text, images, or music, based on their training data. Key to GenAI are Large Language Models (LLMs) and Foundation Models (FMs), designed to produce human-like text from extensive datasets. Central to GenAI are Large Language Models (LLMs) and Foundation Models (FMs),  which are designed to understand and produce human-like text. These models, trained on vast datasets, enable the generation of coherent and contextually relevant content.

Top 4 business benefits of GenAI applications for enterprises

Gartner forecasts that by 2026, over 80% of enterprises will integrate GenAI APIs or models into their operations, a dramatic increase from less than 5% in 2023.

Business benefits could include:

Enhance decision making

• Scenario planning
• Simulation & hypothesis testing
• Causal inference
• Decision support systems

Optimize business processes

• Document processing
• Data augmentation
• Process optimization
• Real-time decision making

Boost productivity & creativity

• Conversational search
• Summarization
• Content creation
• Code generation

Enhance customer experiences

• Chatbots
• Virtual assistants
• Conversation analytics
• Personalization

Enhance your decision making

Imagine a finance team at a global enterprise that regularly faces high-stakes decisions. Traditionally, analysts would sift through historical data, market trends, and economic indicators to forecast outcomes. Now, with GenAI, this process is not only faster but significantly more accurate.

For instance, the AI model analyses millions of data points from diverse sources in real-time, spotting correlations that even the most seasoned analyst might miss. The team can then leverage these insights to make split-second decisions on investments, risk management, and market strategies, ensuring they stay ahead of the competition.

Optimize business processes

Consider a manufacturing plant that operates 24/7. Equipment failures and unexpected downtime can lead to massive losses. With GenAI, the plant’s maintenance team doesn’t just react to problems — they prevent them.

GenAI systems continuously monitor machinery, analysing patterns and predicting failures before they happen. Picture a team receiving alerts on their tablets, detailing exactly which component is at risk and when it needs attention.

This proactive approach not only prevents costly disruptions but also optimizes resource allocation, as maintenance is performed only when truly necessary, reducing waste and boosting overall efficiency.

Boost productivity and creativity

In entertainment and media, staying relevant requires constant innovation. Picture a content creation team tasked with developing a new series. With GenAI, they’re not starting from scratch.

The AI suggests fresh storylines, generates character arcs, and even predicts audience preferences based on current trends. As the team collaborates with GenAI, they are able to push creative boundaries, producing content that resonates more deeply with audiences.

This blend of human creativity and AI-driven innovation results in ground breaking content that captivates viewers and sets new industry standards.

Enhance customer experience and engagement

Imagine an e-commerce team using GenAI to craft personalized recommendations for each customer based on their behaviour and purchase history. Instead of broad segments, customers receive tailored emails showcasing products they’re likely to love, boosting conversion rates and loyalty.

GenAI enables enterprises to deliver highly personalized and timely customer interactions, from tailored product recommendations in e-commerce to proactive support in retail, while also building emotional connections through personalized messaging. This enhances customer satisfaction, drives loyalty, and fosters long-term business growth.

GenAI use cases for 6 industries

GenAI is transforming various industries by enhancing functionality and efficiency.

Inventory & Sales Analytics

A textile retailer who once struggled with excessive inventory — leading to financial losses and wasted shelf space — can now leverage GenAI to analyse vast datasets like sales history, media trends, and customer preferences. This advanced analysis helps them accurately predict demand, enabling precise ordering for the upcoming season. As a result, they maintain optimal stock levels, reduce waste, free up shelf space, and improve profitability.

Risk Analytics

Retail chains often face challenges like fraudulent transactions and supply chain disruptions. By employing GenAI-driven risk analytics, they can detect anomalies in real time, allowing for intervention before issues escalate. For instance, a sudden spike in returns might be flagged as potential fraud, enabling immediate action and ensuring smooth operations.

Supplier Analytics

Managing multiple suppliers can be daunting, but with GenAI, retailers can assess and manage supplier performance more effectively. By analysing data on reliability, delivery times, and product quality, they can identify underperforming suppliers and make informed decisions, such as seeking alternatives or negotiating better terms, thereby strengthening their supply chain.

Customer Experience

Enhancement Retailers can significantly enhance the customer experience by offering personalized recommendations powered by GenAI. Analysing customer behaviour and preferences allows them to create tailored marketing campaigns and product suggestions, resulting in a more satisfying shopping experience that fosters long-term loyalty.

In the retail industry, information about inventory, sales, and suppliers is now readily available at the click of a button. Businesses can understand risks and make more informed decisions, which was unimaginable just six months ago.

Smart Claims Management

Insurance companies often struggle with slow claims processing, but GenAI offers a solution by automating this process, making it faster and more accurate. When a customer files a claim, the AI can instantly review documentation, verify information, and process the payment, reducing the waiting time from weeks to just days.

Data Enrichment

To improve risk assessment models, insurers can use GenAI to enrich their data by integrating information from various sources, including social media, weather forecasts, and historical claims data. This comprehensive view allows for more accurate risk assessments, leading to better pricing and more effective customer service.

KYC

With GenAI, customer service teams can access a complete view of each policyholder’s profile. The AI compiles data from various touchpoints, ensuring compliance with KYC regulations and enabling more personalized service, such as offering relevant policy updates or efficiently responding to inquiries.

Even in traditional industries like insurance, GenAI is levelling up smart claims management. Companies are beginning to use intelligent document processing to ensure high-quality, consistent service.

Route Optimization

Logistics companies can significantly reduce delivery times and fuel costs with GenAI-driven route planning. By analysing traffic patterns, weather conditions, and delivery schedules, GenAI creates the most efficient routes, leading to faster deliveries and happier customers.

Demand Forecasting

Airlines anticipating surges in bookings, particularly around holidays, can benefit from GenAI’s predictive analytics. This technology forecasts demand, allowing airlines to allocate resources effectively, whether by adding more flights or adjusting prices to maximize revenue.

Supply Chain Optimization

Global shipping companies looking to enhance efficiency can rely on GenAI to optimize supply chain operations. By analysing data on shipping routes, fuel consumption, and port congestion, GenAI helps reduce delays, lower costs, and improve overall service quality.

Government agencies are beginning to use GenAI to make their services much more user-friendly. Citizens can now retrieve information more easily, improving their experience with public services.

Citizen Services

Self-help government services can be made more accessible and user-friendly with AI-powered chatbots. These virtual assistants provide quick and accurate information, such as answering tax filing questions or guiding users through permit applications, enhancing the overall citizen experience.

Intelligent Document Processing and Validation

Government agencies overwhelmed with paperwork can turn to GenAI for automating document processing and validation. Whether it’s passport applications or business licenses, GenAI ensures faster turnaround times and reduces the likelihood of errors.

Risk Analytics

Banks can leverage GenAI to assess and manage financial risks by analysing market trends, credit scores, and transactional data. This allows them to identify potential threats and take preventative measures, minimizing losses and safeguarding their operations.

Collection Analytics

Improving debt collection processes becomes more efficient with GenAI. By analysing customer payment histories and behaviour patterns, the AI can prioritize collections, increasing recovery rates and streamlining operations.

Portfolio Analytics

Investment teams can optimize portfolio performance using GenAI, which provides insights into market movements and potential risks. These insights help the team make informed decisions, maximizing returns for their clients.

Customer Analytics

Banks aiming to offer personalized banking experiences can utilize GenAI to analyse customer data, providing tailored financial advice and recommending relevant products. This personalized approach enhances customer satisfaction and loyalty.

Manufacturing companies are beginning to use GenAI to make high-quality decisions in inventory planning and supply chain management. This technology provides real-time insights that were unimaginable just a short time ago.

Inventory Monitoring

Manufacturers can track and manage inventory in real-time with GenAI. The AI monitors stock levels, predicts demand, and alerts the team to reorder materials before they run out, preventing production delays and reducing costs.

Supplier Analytics

Manufacturing companies can assess supplier performance using GenAI by analysing delivery times, product quality, and pricing. This allows them to choose the best suppliers, negotiate better terms, and maintain a reliable supply chain.

Process Automation

Factories can optimize production processes with GenAI, reducing downtime and increasing efficiency. This automation leads to lower costs and higher output, making operations more streamlined.

Production Planning

Production teams can create efficient schedules using GenAI by analysing data on resource availability, machine capacity, and market demand. This ensures orders are fulfilled on time and within budget.

The post GenAI for Enterprises: Top Benefits, Impact And Industry Use Cases appeared first on Cloud Kinetics.

• Fraudulent transactions across Europe are an estimated €1.8 billion per annum.
• The number of bank frauds in India was up 166% in FY24.
• In the United States 26% of adults surveyed said they had personally experienced bank/credit fraud.

The explosion of online banking, neobanks, fintechs and financial applications has also made it easier for scammers to strike, making it vital to spot anomalies in transactions and strange behaviour to catch fraud early. In this scenario, Artificial intelligence (AI), Generative AI & Machine Learning (ML) are new sentinels for safe and secure business operations and technology, helping banks, financial services, and insurance (BFSI) companies stay one step ahead of fraudsters.

According to one survey, 62% of UK and US based large/mid-sized businesses intend to deploy AI-based solutions to combat the issue.

Power of AI in fraud detection: Traditional security options vs AI-driven solutions

• AI can “learn” from past fraud cases, helping ML algorithms more accurate with time. An AI model flags suspicious data/anomalies in transactional and behavioural data.
• As self-learning models, AI gets smarter over time, reducing the likelihood of repetitive errors and minimizing false positives.
• AI can not only alert the humans overseeing the systems to potential fraud, but also take action by blocking transactions or removing suspicious attached files.

AI and ML can give banks and financial companies a huge advantage with a “two-steps ahead” approach to security and risk management. For instance, for a large multinational bank, fraud detection traditionally involved wading through mountains of data, reading endless reports, and manually checking every suspicious transaction. It was a slow and painful process, often leading to delays in spotting fraud. Sometimes, customers would even have to report the problem themselves, which could mean losing a lot of money before the bank could fix the issue.

The power of AI-backed fraud prevention means that the same bank can now process copious volumes of data in real time, monitor all activity including transactions as they happen. When a possible high risk event begins to occur, it escalates this to the top of the list for review on priority. The bank can now intervene as the fraud is occurring and prevent it from happening or reduce further potential loss. Overall, this can mean better customer satisfaction with fewer losses incurred.

Using AI in fraud detection means fast detection – since AI algorithms act instantly to freeze/block a transaction/account, and offers increased accuracy over traditional methods – since AI applies dynamic rule setting, learning from itself, rather than just predefined rules. Over time, this results in cost optimization as the long-term cost of prevention vs reaction is lower.

Business impact of AI for fraud prevention

AI can be applied in multiple ways to help mitigate the risk of fraud:

• AI-driven analytics platforms can integrate diverse data sources (financial data, market data, customer data) to provide a comprehensive view of risk exposure.
• GenAI for real-time fraud detection identifies suspicious patterns of behaviour through comprehensive data analysis; this helps block and prevent potentially fraudulent activity. 
• AI-powered alert prioritization is used to classify alerts by risk level, ensuring that higher risk cases get assigned for review and intervention first, which means speedy intervention and protection for the business.
• Predictive analytics help determine future risk based on constantly updated data. AI & ML can minimize false positives, making for a seamless customer experience while ensuring security.
• Data-driven operations backed by AI/ML and robust analytics help ensure regulatory compliance and support KYC verification.
• Automation along with a strong GenAI/AI/ML powered business analytics and data engine supports scalability and boosts operational efficiency.

Use Cases for BFSI | How AI helps in fraud prevention

AI-based use cases for fraud prevention in the banking and financial services sectors can take on various forms:

1. Real-Time Anomaly Detection: Systems using GenAI can detect fraud early by learning normal behaviour and spotting unusual activity or deviations that might indicate account takeovers from identity theft or phishing. This improves speed – something that’s crucial when dealing with fraud, where every minute counts.

GenAI-powered behavioural analysis can monitor app usage, banking transactions, payments, or any other financial transaction across channels/touchpoints in real time and flag off potential threats like unusual spending pattern/unauthorized account access, blocking them and preventing fraud.

AI-backed fraud detection enables faster action, better communication and quick resolution. Traditionally, we have relied on programming languages to identify any aberrations. With ML algorithms, statistical analyses and AI, we can implement a framework that easily identifies the current unusual behaviour as well as new behaviour in the future without too many changes in the program and environment. This translates to cost savings, cuts developer time and reduces time to market/time to go live.
Dipti Pasupalak, Data & Analytics Architect, Cloud Kinetics

2. Automated Fraud Reporting and Reduced Manual Reviews: AI and ML allows for automated fraud reporting and reduces the need for manual reviews. GenAI generates suspicious activity reports (SARs) incorporating millions of data points. With a lower burden on analysts, finance and IT teams, their time can be freed up to be used to propel business growth, enhance solutions and drive innovation.

Automation also makes the process of identifying investment fraud, payment fraud, or card fraud faster, more efficient and often more accurate, with lower instances of false positives.

3. Enhanced Authentication with AI: Secure authentication powered by AI can be improved with GenAI and reduce risk in case of forgery or identity theft.

GenAI can help refine algorithms used for recognition and verification, thereby making traditional biometric verification methods more effective and limiting access to only legitimate users. This cuts the risk of unauthorized access/ account takeover fraud.

Use Case | Seamless User onboarding & authentication with AI/ML-powered solutions from AWS: In the online registration process for an account, using ML-powered facial biometrics – with pre-trained facial recognition and embedded analysis capabilities, ID verification, user onboarding and authentication – can be done securely with no need for prior ML expertise in-house.

4. Detecting Variations in Usage Patterns: AI is able to analyse metadata to detect variations from the norm that might be missed in manual reviews by the human eye. As fraudsters begin to use sophisticated methods including AI to commit fraud, the use of AI as a defense against things like deepfakes will be critical.

Take for example a scenario where a customer has been duped into sharing their net banking details with a fraudster. Normally transactions after this would not be flagged since the data compromise has not occurred on the bank’s system. But AI-based risk monitoring software will spot any unusual pattern in the transactions or amounts not in line with their normal transactions, or even things like screen resolution, currency or language used and flag it for manual tele-verification in real time, more swiftly than older methods.

Use Case | Fraud prevention with Snowflake’s scalable multi-cluster shared data architecture and advanced data governance: This can help protect merchants from fraud and risk. Snowflake-powered fraud prevention models are able to identify bad actors, detect attack vectors and block account takeover attempts.

5. Offline Fraud Prevention: AI-powered video analytics can flag off suspicious behaviour at ATMs and branches that may be linked to ATM skimming, usage of stolen cards or cheque forgery.

Use Case | Geospatial analytics and AI for fraud detection from Databricks: Geospatial data, machine learning and a lakehouse architecture from Databricks help FSI clients better understand customer spending behaviours and spot abnormal credit card transaction patterns in real time. This enhances the fraud prevention and detection capabilities of the organization, which in turn reduces losses and helps cement customer trust.

Building an AI-backed fraud detection strategy

Banks and financial institutions aren’t the only ones with an eye on AI. According to Deloitte’s Center for Financial Services, fraud losses in the United States could hit US$40 billion by 2027, on the back of GenAI.

With fraudsters already using AI, industry needs to quickly adopt an AI-backed defense as well. Here’s your roadmap:

• Create a cross-functional fraud management team: Drawn from IT, operations, compliance, legal, data sciences
• Build a multi-layered fraud detection strategy: Use AI in tandem with traditional anomaly detection systems, encryption, multi-factor authentication etc.
• Implement the right environment and tools: These must be compatible with existing infrastructure and scalable and effective. Banks must modernize their infrastructure to effectively leverage AI for fraud prevention.

By migrating to the cloud or adopting a hybrid approach and establishing a robust data platform, banks can ensure timely access to high-quality data. This real-time data empowers AI, ML, and generative AI systems to analyze patterns, identify potential fraud, and enable rapid intervention.

• Follow transparent & ethical data usage: Adhere to customer privacy norms and practise ethical data usage
• Monitor & update regularly: Retrain with new data to stay effective against new types of fraud
• Run simulations: Run controlled realistic fraud attack simulations to check robustness of the systems in place and keep ahead of advanced fraud attacks

Building an effective AI-backed fraud detection strategy into your organization requires an overall commitment to a security-conscious culture. In addition to the AI, ensure every “human firewall” is well armed to respond to fraudulent activity with regular training and a culture that encourages a security-first approach. Dipti Pasupalak, Data & Analytics Architect, Cloud Kinetics

The post How Banks & Financial Services Can Fight Fraud With AI-Driven Analytics appeared first on Cloud Kinetics.

API or application programming interface is, simply put, the code that allows two different software programs to communicate with each other. It gives access to data or a service functionality in a database or app. This, of course, has far-reaching applications.

Why API-first? Top benefits for your enterprise

APIs can help enable interactions with other apps, smart devices, and human users. And today, companies in the know have begun to leverage APIs to drive digital transformation. For many organizations, it can help support their new platform business models.

An API-first approach has many proven benefits.

Here are two use cases where an API-first approach makes a tangible difference:

An API-first e-commerce platform allows for seamless integration with payment gateways, logistics providers, and customer relationship management (CRM) systems. This translates to a smoother user experience, faster checkout process, and efficient order fulfillment.

A financial services app leveraging an API-first approach can easily integrate with stock exchanges, credit bureaus, and other financial institutions. This enables features like real-time stock quotes, credit score checks and personalized investment recommendations.

What does “good API design” look like?

You can future-proof API architecture with good design.

When it comes to API design, thumb rules to follow are to design APIs with scalability and agility in mind. Leverage microservices and API gateways to create modular, loosely coupled APIs, and employ open standards and versioning to ensure compatibility and to future-proof your API investments.

These features translate to some best practices that need to be synonymous with your process.

10 best practices for an API-first development strategy

By following these best practices, you can establish a robust and successful API-first development strategy. The focus on business alignment, technical excellence and continuous improvement will ensure your APIs deliver real value and contribute to the overall success of your software projects.

Business Business Practices

Ensure the design is aligned with business goals: Don’t just build features, build value. Ensure APIs directly contribute to achieving overarching business objectives, whether it’s driving revenue growth, streamlining partner integrations, or fostering a developer ecosystem.

Common goals, a clear path, shared processes – that’s the recipe for successful app development. When there’s a strong team of principals that get both business and tech speaking the same language, that’s where we see the wins. Lucas Eagleton, Technology Principal, Cloud Kinetics

Start stakeholder collaboration early: Get stakeholders in the loop from the start – potential end users, business teams, designers, the dev team, the infrastructure team, and anyone else who may be relevant. You will be able to better define data requirements, access control requirements, as well as API use cases.

Consider business impact (for old and new users) when versioning: While you introduce new features in new API versions, also keep in mind compatibility for current/existing clients on the API.

Technical Best Practices

Follow reusable well-defined design principles: Modular API components will help with seamless integration into different applications and bring down development effort/time. Implement well-defined design principles like RESTful architecture for consistency and ease of adoption. Enforce clear naming conventions, resource structures, and response formats (JSON, XML) for efficient integration.

Build security from the start: Prioritize security throughout the API development lifecycle. Implement strong authentication and authorization mechanisms (OAuth, JWT) to control access and prevent unauthorized usage. Utilize secure communication protocols (HTTPS) and robust data validation to safeguard sensitive information.

Implement continuous testing: Your testing strategy should span the entire development process and include unit testing, integration testing, as well as performance testing. Comprehensive testing helps improve reliability, functionality, as well as scalability of the API.

Developers are your API champions. Invest in a comprehensive developer portal with clear, interactive documentation, code samples, tutorials, and sandboxes for testing. Bring the team in early if you can. If not, a large amount of rework/rewriting of components/V2 of newly released products will need to be done because it doesn’t fit the requirements that got considered too late. Ideally, if you want to combine disparate solutions, build them in at the start with a bigger picture view.

Create style guides: Be sure to create and circulate an API style guide at a company level to bring consistency across APIs. This should cover data formats, conventions, security best practices, and error-handling approaches.

“By helping design the API first, we have pieces that flow out so that everyone has a central documentation to refer to. Sure, there might be some reworking and changes, but this enables parallel teams to work on it in tandem,” says Lucas Eagleton, Technology Principal, Cloud Kinetics

Monitor API usage: Monitoring API usage patterns can help you spot areas for improvement and keep it relevant vis-a-vis customer needs.

Use automation: Automation tools can greatly streamline workflows and help cut the time and manual effort involved in API documentation generation, testing, and deployment.

Test, Test, test the API: Do remember to test the API at various stages to see how tweaks or changes or additional features are impacting overall functionality as well as security. Collaboration is key so get all stakeholders looped in early on. And then test, test, test! Keep this up throughout the development lifecycle.

API testing tools help cover a range of scenarios and test it on all the key features/areas earlier in the life cycle, a trend called “shifting left”. This helps spot issues as soon as they come in and allows teams to fix them right away. Punit Chheda, Vice President – Enterprise Architecture & Consulting, Cloud Kinetics

In an API-first approach, this is mission critical because an API that is not performing, has availability issues or errors can result in customer churn and have a negative impact on your business. The goal is to catch issues well in time so that they do not flow into production and eventually impact users. By executing API tests within CI/CD pipelines, teams can manage more rapid iterations, frequent releases, and simultaneously also lower the risk of bugs. Overall, this helps lower the risk of eroding customer trust and protects the brand image

API in action: A Cloud Kinetics case study

Here’s an instance of how we streamlined global operations for a food manufacturer with Mulesoft API deployment. A leading global food and confectionery company faced challenges integrating their complex supply chain, logistics and manufacturing systems spread across various locations. Streamlining these integrations was crucial for optimizing operations and ensuring efficient data flow.

Challenges for the enterprise included:

• Global system disparity: The company’s supply chain, logistics, and manufacturing systems operated independently across different regions, hindering data visibility and process automation.
• Integration complexity: Integrating these disparate systems required a robust and scalable solution to manage the complexity of data exchange.

The company partnered with Cloud Kinetics to implement an API-led integration strategy using Mulesoft CloudHub, a hybrid integration platform. Here’s how Cloud Kinetics addressed the challenges:

• Mulesoft CloudHub implementation: Cloud Kinetics chose Mulesoft CloudHub as the central platform for API deployment due to its scalability, flexibility, and pre-built connectors for various enterprise applications.
• API design, development, and deployment: The team designed, developed, and deployed foundational APIs that could seamlessly connect the disparate supply chain, logistics, and manufacturing systems.
• Standardized integration patterns: Established standardized integration patterns to ensure consistency, reusability, and maintainability of the APIs across the entire ecosystem.
• Expert support: The team provided ongoing support throughout the development lifecycle, including troubleshooting any integration or workflow issues, and reviewing API design, code, and security posture.

Outcomes that stood out for the client:

• Streamlined operations: The successful API deployment enabled seamless data exchange between previously siloed systems, significantly streamlining global supply chain, logistics, and manufacturing operations.
• Reusable APIs: The creation of foundational APIs provided a reusable foundation for future integrations, saving development time and resources.
• Standardized integrations: Standardized integration patterns ensured consistency and maintainability of the API landscape, simplifying future modifications and enhancements.

Mulesoft and a well-defined API strategy can empower businesses to overcome complex integration challenges and achieve operational efficiency across a global footprint.

At Cloud Kinetics, we’ve seen first hand how an API-driven approach can help our customers drive growth through new business models. It translates to faster collaboration and enables enterprises to quickly on-board new product offerings and enhance customer experience. Harsha Bhat, Senior Director – Applications, Cloud Kinetics

Prioritize API design and design your API with reusability in mind. Remember, the API world is constantly changing. Be prepared to iterate on your API design based on usage and feedback. This is an investment that pays off in terms of faster development, increased agility, and a more robust and scalable software ecosystem – one that has far-reaching business impact.

The post API-First Approach To <br>Application Development: 10 Best Practices appeared first on Cloud Kinetics.

For traditional banks this doubles up as an opportunity and an urgent reminder. To compete effectively with digital banks and fintechs and thrive in this evolving financial ecosystem, senior IT/business decision-makers at established banks must prioritize modernizing legacy systems and embracing the Open Banking paradigm.

Advancing customer experience with open banking

Open banking ushers in a new era of convenience and financial control. It fosters financial inclusion, more personalized services and convenience for customers. Open banking also increases the competitiveness of banks by enabling them to provide a diverse suite of financial products & services at reduced operational costs.

Some open banking use cases include:

• Instant payments and fund transfers directly from third-party applications such as PayNow in Singapore and Unified Payments Interface (UPI) in India.
• Account aggregation features for customers to access and manage savings & deposit accounts, loans, credit cards and investments from a single application.

From monolith to microservices: The application modernization journey to open banking

For most banks, successful adoption of open-banking standards will mean substantial re-architecting of their current application estate and IT infrastructure, and accelerated integration with third parties to on-board new products & services from partners. This is a critical success factor in the open banking ecosystem.

To seamlessly integrate with various third parties through APIs, banks will require an enterprise-wide adoption of microservices and API-based architecture to consume data from core banking and other legacy backend systems. This will support agile delivery, provide scalability and flexibility with multi-cloud / hybrid cloud deployments.

A microservices-based application modernization approach will eventually enable banks to drive growth through new business models, collaborate with partners to quickly on-board new product offerings and enhance customer experience.

1. Building the optimal microservices-based architecture for open banking

The application modernization journey and timelines will depend on the product roadmap, technology readiness and current IT architecture of the bank.

Banks with monolithic applications, legacy core banking systems and legacy integration platforms will need to adopt a phased approach to minimize business risks and potential disruptions. A phased approach will also allow banks to on-board partners and build marketplace offerings like digital wallets, lending and insurance services for customers in an incremental manner.

The figure here below illustrates an indicative microservices-based reference architecture for a bank along with key components such as API gateway, service orchestrator and microservices-based core services.

• The first step in the application modernization journey would involve building a service orchestration layer (Figure 1, point# 2) to efficiently manage commonly used cross-cutting concerns and non-functional requirements such as security, configurations, log aggregation, distributed tracing, service discovery and circuit breaker.
• All the above services can be developed as common components designed to ensure the core business logic is separated from cross-cutting non-functional concerns.
• The service orchestration layer along with an API gateway will interface with the core banking platform and expose APIs to both internal and external parties for accelerated integration.
• In subsequent phases, banks can start decomposing their monolithic core applications using various Microservices design patterns such as Strangler and Sidecar aligned to product domains such as account services, payment services and underwriting services (Figure 1: point# 3). For example, account services would include features such as account opening, customer onboarding and account updates.

2. Microservices-based application modernization: Challenges & way forward

Transitioning from a monolith to microservices can be a complex but rewarding undertaking for banks. Often, banks are concerned about technical complexity, security challenges and change management.

The right application enablement partner will equip you with a comprehensive roadmap, from initial strategy and architecture design to seamless implementation and ongoing support, ensuring your microservices journey is smooth and efficient.

3. Modernizing banking applications with Cloud Kinetics

Cloud Kinetics helps banking & financial services customers to amplify the value of cloud with our Application Modernization approach, driving business agility & flexibility.

Our modernization competencies focus on Application Portfolio Assessment, Microservices Architecture, Serverless, API Integration, Security & Compliance combined with DevOps & Continuous Delivery Approach. Our strategic partnerships with industry leaders like AWS, Azure, GCP (hyperscalers), Kong (API gateway), CAST (portfolio assessment) enable us to deliver a seamless solution for our customers.

Customer impact story: App modernization, cloud-native design & a one-stop digital marketplace for a leading fintech

Our customer is a leading fintech company that facilitates financing through credit guarantee schemes for micro, small & medium enterprises (MSMEs). They felt their current platform was lacking the comprehensiveness, accessibility and user-friendly experience that MSMEs now demand in a digital-first world.

With Cloud Kinetics, the customer embarked on a transformation journey to modernize their platform and provide MSMEs with a one-stop digital marketplace, with opportunities for cross- and up-selling products and services.

Cloud Kinetics developed the digital lending platform on AWS, using microservices architecture to create a cloud-native lending platform that was secure, scalable and adaptable to evolving needs. Cloud Kinetics also built a digital marketplace for the customer that expanded beyond loans, offering MSMEs financial products like working capital solutions, insurance options, and seamless integration with credit guarantee schemes. The interface enabled the bank to offer MSMEs a more intuitive experience and better manage their financial health.

The post Unlocking Open Banking’s Potential: Microservices As The Key To App Modernization appeared first on Cloud Kinetics.

Based on inputs from Cloud Kinetics FinOps team  

Operating in the cloud promises a boost to agility, innovation, time to market and more. But if you don’t have the centralized coordination and collaborative approach that FinOps brings, you could be wasting millions in cloud spends every year!

For many organizations, investments in cloud spend run into the billions every year as they tap into the agility, reliability, and flexibility that the cloud offers. With the sudden increase in cloud investment has come the problem of excessive and wasteful spending. If your organization is on the verge of implementing FinOps but you’re unsure of what it entails or have cloud spending related concerns, answers to these top 5 most common questions on cloud costs should clear things up.

Over 30% of cloud spend is reported to be wasted every year. This number is projected to hit $30 billion by the end of the year.

Implementing FinOps – cloud financial management practices designed to maximize business value – is the most effective way for organizations to gain better control on cloud spend, avoid waste and keep projects running efficiently without compromising on end results. Here are some of the most common questions and concerns our customers, stakeholders and decision-makers have before embarking on a FinOps journey.

Our in-house experts at Cloud Kinetics, led by FinOps Managed Services Director Aubrey Bent, provide insight and pointers based on their extensive experience working with a global client base.

1. What is the best way to kickstart FinOps practices in my organization?

When it comes to FinOps, one step at a time works better than a big-bang approach. Implement the approach in small incremental steps, allowing the organization to fully understand and slowly settle into the new approach. Here’s a roadmap:

• Get senior stakeholder management support and sponsorship.
  Identify pain points like cloud costs, cost overruns or lack of cost visibility and define the goals right from the team composition to the operating model and milestones for the first phase. For instance, you may need to make decisions on things like whether a FinOps centre of excellence team is being set up.
• Create a FinOps roadmap/plan to define the future state.
  This should include an operating model to clearly define what the FinOps team will be doing. A Responsible, Accountable, Consulted, Informed (RACI) model will have to be created explaining what each person’s roles and responsibilities are and identify KPIs to measure the FinOps function and performance of business and application teams.
• Prepare an engagement plan.
  This will help show how you will engage and collaborate with your finance, business, engineering and procurement teams.
• Create an ongoing review plan.
  After about 3-6 months, gradually move onto the next phase where you can implement your KPIs, do some more dashboards for creating useful cost optimization reports, and bring automation in as well. The system will evolve over time and you will get better results.

2. How important is training to FinOps success?

Training and education is paramount to the successful implementation of FinOps. It’s also where the entire exercise can fail.
Get internal teams – such as the business and app teams – together. Conduct these 4 training activities on an ongoing basis.

• Kickoff meeting
• Brownbag sessions to educate teams on the FinOps Framework and tooling
• Implementation of recommended actions, ensuring teams are doing these regularly to achieve cost savings.
• Monthly or quarterly scorecards to measure performance and optimization every month.

Repeat these actions month on month till it becomes part of the team’s DNA – if they don’t do it on a regular basis, FinOps cannot be successful.

3. How do we effectively measure FinOps results – any metrics to look at?

• You can define several measurable parameters. For instance, Reserved Instance or Savings Plan Coverage must be >80% every month. Another metric could be that after 30 days of idle instance, you stop or decommission your instance.
• You could track accountability and enablement with a metric like Cloud Enablement %, the number of business leaders trained and certified / the total number of business leaders in the organization as a percentage.
• The Cost Optimization Realized Savings % measures the ratio of total cloud services optimized to total cloud services optimizable. This brings attention to areas for potential cost savings – pricing optimizations like Committed Use Discounts as well as resource optimizations of wasteful resources that haven’t brought/added any business value. The latter could be from over-sized databases, idle instances, etc.
• Annual Forecast Accuracy % is another useful metric that looks at the ratio of actual annual cloud spend vs the forecast cloud spend per annum. The number stabilizes as the gap between actual and forecast narrows, allowing the business to plan better and avoid any unexpected spikes in spends.

4. What are some typical challenges businesses face when managing cloud spend, and what are some best practices to address them?

FinOps to manage cloud spending might sound simple, but there can be some challenges.

The hardest one is to change the ways of working within an organization especially if someone has come from an on-premises world and is also new to cloud. It needs a mindset shift.

Surveys show that 30% of the challenge is getting the DevOps team to do the actions needed to enable FinOps. DevOps teams tend to already be very busy doing development and project work which is their priority and key focus.

Additionally, FinOps can become complex because of the intrinsic need for collaboration and getting the finance team and the procurement teams involved isn’t always straightforward.

The best way to overcome these issues is to educate them and train them on the benefits and value from FinOps, so that all the teams are ready to come on your journey. FinOps is relatively new to the cloud industry and many people do not know about it, so training sessions and brownbag sessions help build cadence with them and smoothen the process.

The next thing is to try and automate these actions as much as possible so that less manual effort is required from them.

5. What are some tools or services that Cloud Kinetics offers that can help businesses better manage their cloud spend?

Cloud Kinetics has best-in-class partnerships with multiple providers/tools in the field, including Apptio and Spot by NetApp. We have extensively utilized Cloudability by Apptio for FinOps in our engagements with customers as we have found that it meets most of our customers requirements – from easy-to-use dashboards and a benchmarking scorecard to insights into right sizing and unused resources.

Cloudability has APIs allowing one to integrate with DataDog, PagerDuty, ServiceNow etc. For example, if you have a workflow or want to put your billing into one central location like ServiceNow it can integrate as well. This tool can store the data for 1-2 years, allowing for forecasting, right sizing recommendations, unlike native tooling which has a limited window for storage of billing data.

That said, at Cloud Kinetics we ensure we always choose the best-fit solutions and tools for your organization based on your requirements and goals.

You can also download the FinOps 101 Handbook HERE

The post Save Money In The Cloud <br>With FinOps: Your Top 5 <br>Cloud Cost Qs Answered appeared first on Cloud Kinetics.

As more organizations embrace the cloud, securing cloud-native applications has become increasingly complex. Fragmented multi-vendor solutions attempt – and struggle – to shield an attack surface that’s expansive, dynamic and vulnerable. The solution to this growing challenge in cybersecurity could lie in cloud-native application protection platform or CNAPP.

CNAPP is a comprehensive solution that gives DevSecOps and DevOps teams unified automated security that oversees containers, workloads, compliance and more – the entire application lifecycle. It is being used by organizations to crank up security as well as visibility across hybrid and multi-cloud, and private and public cloud environments. But first, let’s see why there’s such a strong case for CNAPP and how it fares over traditional solutions.

Why traditional cybersecurity solutions aren’t always optimal

How did security end up being so complicated? Organizations have grown their cloud investments over time and added solutions and products in phases, organically. The accompanying security products layered over these have also been heterogeneous and operated in silos. The result? DevSecOps is fashioned sometimes from as many as 10 different tools each working in isolation!

Add to that the fact that cloud environments often involve microservices, containerization or serverless architecture. A far cry from traditional IT environments. This is why traditional intrusion detection and firewalls just don’t cut it when it comes to the distributed and dynamic cloud environments of today. These modes of security were designed to serve a fixed network perimeter like a data centre. Not the complex distributed cloud environments that are par for the course today.

The most significant driver is the need to unify risk visibility across the entire hybrid application and across the entire application life cycle. This simply cannot be achieved using separate and siloed security and legacy application testing offerings. – 2023 Gartner® Market Guide for Cloud-Native Application Protection Platforms

Your cloud security & CNAPP

What is CNAPP? A cloud-native application protection platform offers a simplified security architecture to enable enterprises to reduce complexity and costs of security solutions that operate in silos. CNAPP lets a business benefit from a unified continuous security structure without any added investments by way of more manpower or investment in more tools.

The compelling case for CNAPP in cybersecurity

The global CNAPP market is set to grow at 19.9% (CAGR) between 2022 and 2027, to USD 19.3 billion, driven by a growing risk of breaches and reported incidents of cyber threats, an increasing use of cloud solutions, a manpower crunch within the IT security teams in-house, as well as the potential vulnerability posed by an increasingly WFH/remote workforce.

By 2025, 60% of enterprises will have consolidated cloud workload protection platform (CWPP) and cloud security posture management (CSPM) capabilities to a single vendor, up from 25% in 2022. 2023 Gartner® Market Guide for Cloud-Native Application Protection Platforms

CNAPP was built to protect cloud-based infrastructure and applications. The solution is agile, dynamic and scalable. Large existing cloud users like ISVs and SaaS companies have begun to see the benefits of CNAPP.

The issue of combined risk is something CNAPP is capable of dealing with. While security solutions like cloud infrastructure entitlement management (CIEM), cloud workload protection platform (CWPP) and cloud security posture management (CSPM) do offer data on vulnerability and risk they are unable to come together in a way that – as Gartner puts it – connects the dots. CNAPP identifies the effective risk across the various layers that comprise cloud-native applications, helping prioritize risk and easing the burden on overstretched security and developer teams.

Here are some features of CNAPP that make it the smart choice for enterprises that operate in the cloud.

• Is a combined cloud security solution
• Is purpose-built for cloud-native environments
• Is integrated with the app development life cycle
• Does not add additional complexity to the application
• Supports scanning and quick response to any misconfiguration

How to choose a CNAPP solution

There is a palpable shift underway in the market, to consolidate cloud security solutions and benefit from the ease and visibility that a single CNAPP solution brings.

If you are considering CNAPP, here is a quick guide to choosing the right partner. The exercise will be most effective if those doing the selection are drawn from the various teams that will be involved with or impacted by the solution – namely, developers, development security, app security, cloud security, workload security and middleware security teams.

Make a CNAPP decision for your cybersecurity!

Finding a good partner for your CNAPP solution is critical and Cloud Kinetics has the expertise you need. Cloud Kinetics offers CNAPP in partnership with Plerion, an all-in-one Cloud Security Platform that supports workloads across AWS, Azure and GCP. If securing your entire cloud with an all-in-one cloud security platform is on your mind, we can help!

Cloud Kinetics is an award-winning, certified cloud transformation and managed services partner headquartered in Singapore and operating globally. We use cutting-edge platform-driven services to accelerate and secure our clients’ digital and business transformation journeys. Get in touch with our cloud experts for a non-obligatory discussion at contactus@cloud-kinetics.com

You can also download the CNAPP Cloud Security Handbook Here

The post Cloud Native Application Security (CNAPP): A Game Changer In Cybersecurity appeared first on Cloud Kinetics.

According to the HashiCorp State of Cloud Strategy Survey, a significant 89% of participants emphasized the importance of security for successful cloud implementation. However, as organizations embrace the cloud, they face the challenge of reevaluating their approach to securing applications and infrastructure. The traditional notion of security, defined by a static and IP-based perimeter, is evolving into a dynamic and identity-based paradigm with no clear boundaries. This transformative concept is commonly referred to as zero trust security.

The increasing adoption of zero trust reflects the rising security challenges faced by enterprises. Organizations have seen their attack surfaces expand as remote work policies become more prevalent and endpoint devices are used outside the corporate network. Concurrently, the frequency and intensity of cyberattacks have significantly increased.

Zero Trust is based on three guiding principles that shape its implementation:

Verify explicitly: The Zero Trust paradigm does not assume trust by default. Users must actively request access to resources, and they must provide proof of identification. Authentication and authorization are required depending on a variety of data points, such as user identification, location, device, service or workload, data classification, and anomalies.

Impose least-privileged access: Each user is only given the privileges required for their work. This restricts users to just-in-time and just-enough access, employs risk-based adaptive controls, and implements data security measures. As a result, the risk of unintentional or purposeful misappropriation of corporate assets is reduced.

Assume breach: Until proven otherwise, every user within the organization, including employees, contractors, partners, and suppliers, is regarded as potentially malicious. To guard against this risk, security measures should be put in place. Access must be segmented by network, user, device, and application, and data must be encrypted. Analytics must be used for threat detection and better security.

Top use cases of Zero Trust

Reducing business and organizational risk: Zero-trust solutions ensure that applications and services can communicate only after verification based on their identity attributes. This approach reduces risk by uncovering the presence of assets on the network and how they communicate. Moreover, zero-trust strategies eliminate overprovisioned software and services while continuously verifying the credentials of every communicating asset.

Gaining access control over cloud and container environments: The migration to the cloud raises concerns regarding access management and loss of visibility. Zero-trust architecture addresses these concerns by applying security policies based on workload identities directly tied to the workloads themselves. This approach ensures that security remains closely integrated with protected assets, independent of network constructs like IP addresses or ports, and guarantees consistent protection as the environment evolves.

Thorough inspection and authentication: Zero trust operates on the principle of least privilege, assuming every entity to be hostile. Each request undergoes a thorough inspection, including authentication and permissions assessments for users and devices, before granting trust. Continual reassessment occurs as contextual factors change, such as user location or accessed data. By eliminating trust assumptions, even if an attacker infiltrates the network through a compromised device, their ability to access or steal data is restricted due to the zero-trust model’s secure segment isolation.

4 reasons organizations are opting for Zero Trust security

Enhanced cybersecurity: Zero trust models enable companies to establish more effective cybersecurity practices. This provides reassurance that even in the event of a cyberattack, the data remains secure from malicious actors.

Compliance support: Zero trust models help organizations meet compliance requirements, such as HIPAA regulations. By implementing a zero-trust approach, companies can ensure compliance without worrying about potential issues arising later due to non-compliance.

Risk reduction: By allowing access only when needed and limiting unnecessary access, zero-trust models reduce risks for businesses. This approach protects against both internal threats like malware infections and external threats like phishing attacks and ransomware.

Comprehensive data protection: With a zero-trust model, you can have peace of mind knowing that your data is safeguarded. This approach covers a wide range of threats, providing protection against various internal and external risks.
By following the core principles of zero trust, organizations can strengthen their security posture, comply with regulations, reduce risks, and ensure the safety of their valuable data.

Zero trust security with HashiCorp

HashiCorp offers solutions for enterprises that need zero trust security for multi-cloud environments. It manages secrets across multiple clouds and private data centres, enforces security with identity and provides governance through policies. HashiCorp Vault enables enterprises to centrally store, access, and distribute dynamic secrets like tokens, passwords, certificates, and encryption keys across any public or private cloud environment. Unlike burdensome ITIL-based systems, HashiCorp solutions issue credentials to both people and machines in a dynamic fashion, creating a secure, efficient, and multi-cloud solution suited to today’s insecure world.

It’s part of the company’s “zero trust” security which secures everything based on trusted identities. Organizations can use zero trust to manage the transition to the cloud while maintaining the level of security required, one that trusts nothing and authenticates and authorizes everything.

There are now thousands of companies who seek to leverage the cloud (whether hybrid or multi-cloud) to run mission-critical workloads. It’s imperative that they seriously consider zero trust to secure access to authorized personnel. That’s where Cloud Kinetics and HashiCorp can help significantly.

Organizations are rethinking how to secure their apps and infrastructure on the cloud. Security in the cloud is being recast from static, IP-based (defined by a perimeter) to dynamic, identity-based (with no clear perimeter). This is the core of zero trust security.

This is especially true with emerging and booming markets. Sandy Kosasih, Cloud Kinetics Country Director for Indonesia, says, “HashiCorp’s approach to identity-based security and access provides a solid foundation for companies to safely migrate and secure their infrastructure, applications, and data as they move to a multi-cloud world.” Suhail Gulzar, HashiCorp’s Regional Manager of Solutions Engineering for Asia, adds: “Companies use different identity platforms for federated systems of record. Leveraging these trusted identity providers is the principle of identity-based access and security. Our products provide deep integration with the leading identity providers.”

How does zero trust enable human-to-machine access?

“Traditional solutions for safeguarding user access used to require you to distribute and manage SSH keys, VPN credentials, and bastion hosts, which creates risks of credential sprawl and users gaining access to entire networks and systems. Cloud Kinetics deploys HashiCorp’s Boundary solution to secure access to apps and critical systems with fine-grained authorizations that don’t require managing credentials or exposing your entire network. This is an excellent security feature to protect the core network.”Fitra Alim, Cloud Kinetics Country Technology Officer

As security challenges continue to grow, embracing the zero-trust model becomes increasingly crucial for organizations aiming to safeguard their valuable assets from the ever-evolving threat landscape. If you have any questions about improving your cybersecurity practices with zero trust security, get in touch with us. Cloud Kinetics security specialists will be happy to have a non-obligatory discussion with you.

The post Zero Trust Cloud Security: Strengthen Cybersecurity & Safeguard Valuable Assets appeared first on Cloud Kinetics.

This unprocessed data is collectively known as Big Data, and it is now a business’s most precious asset as it provides actionable insights that can make or break a business. But with larger volumes and more complex data being generated daily, more sophisticated analytics is needed to modernize applications and the data interpretation process for the best accuracy – which is where Big Data analytics comes in.

Big Data use cases for businesses

Businesses have many ways to collect personal, behavioural and engagement data from customers, ranging from tracking their browsing habits on their websites to more traditional surveys and feedback forms. On websites, businesses can use cookies to track a customer’s purchase journey and learn everything from how long they spend browsing to how likely they are to drop off at point of purchase. It can also tell brands what offerings are most popular, when customer traffic is at its highest or lowest and how customers are discovering the site. Social media is also one of the best ways for brands to engage with and learn about their customers. Brands can learn the demographics of their target audience based on social media profiles, evaluate the performance of a campaign, product or service based on audience feedback and reactions, and even find out where their customers are based.

Beyond the personalized recommendations and targeted ads, here are 5 innovative ways that brands can leverage big data for deeper customer understanding:

• Predict churn: Traditional churn models often lag behind customer behaviour. By leveraging real-time data like browsing patterns, cart abandonment and engagement metrics, brands can quite accurately predict which customers are likely to churn. Brands can then proactively intervene with personalized incentives or targeted outreach in an effort to control churn.
• Map customer journeys across the business ecosystem: Businesses need to analyse data from all customer touchpoints – websites, apps, physical stores, social media – to understand the complete customer journey. They can then effectively identify friction points, optimize pathways and create seamless omnichannel experiences that cater to individual customer preferences and buying stages.
• Understand the emotional footprints of customers: Businesses need to stretch their learning beyond demographics and purchase history by using text analysis tools such as sentiment analysis, to understand the emotions behind customer reviews, social media mentions, and even customer support interactions. Such an analysis can reveal unspoken frustrations, desires and brand affinities, making it a little easier for brands to tailor their messaging and experiences.
• Decode hidden needs with unstructured data: It is not enough to only focus on structured data like purchase history and demographics. To gather a deeper understanding of customer preferences, businesses need to analyse unstructured data like images, videos and voice recordings from customer interactions. Such details can reveal subconscious preferences, cultural nuances and emerging trends that surveys or focus groups might completely miss.
• Create hyper-personalized feedback loops: The importance of real-time feedback cannot be undermined. Brands can use dynamic surveys and AI-powered chatbots to collect real-time feedback from customers as they interact with the brand. Such data allows brands to instantly customize product offers and recommendations, as well as content based on individual preferences and changing needs.

Processing and analysing Big Data

The collected data is stored in a data warehouse or data lake, where it must then be organized, configured and cleaned for easier analysis. Next, analytics software is used to make sense of the data – it will sift through the data to search for patterns, trends and relationships, which can then be used to build a customer profile or predictive models that can forecast customer behaviour.

Analysing such volumes of data in a short amount of time requires immense computing power and can take a heavy toll on networks, storage and servers. As such, many businesses opt to offload this task to the cloud, which is capable of handling these demands efficiently and quickly. This enables businesses to be more agile and responsive in making customer-centric decisions. Here are some examples of how cloud-based data and analytics solutions can be used to gather, process and translate business data:

• Multi-source data acquisition: Data can be gathered from diverse sources such as business websites, apps, customer interactions, social media and IoT devices.
  • Point of sales and transactional data is a starting point for many businesses.
  • Demographic data enables businesses to understand who is buying what depending on age, gender, economic condition and much more.
  • Altitudinal data  gathered through market research and social media sentiment analysis are other rich data sources too.
  • Social media profiles, reactions to promotional campaigns, products or services are all valuable sources of data.
  • Consumer trends, local preferences and acceptable prices can all be understood from such data. Businesses also get to know about the most popular brands, when consumer traffic is the highest or lowest, and customer browsing styles, among many other attributes.  Cloud-based data integration platforms can then be utilized to unify these various data streams.
• Scalable data warehousing: The massive datasets are stored in secure, flexible cloud data lakes, data warehouses, lake houses like Google BigQuery or Amazon Redshift for efficient retrieval and analysis. Such warehouse tools usually support all types of data, can work across clouds and have built-in business intelligence and machine learning. 
• Data quality management: The data is then cleaned and transformed using cloud-based data cleansing tools to ensure the data is accurate and consistent before analysis. Data management teams must ensure that the data is in alignment with global and domain rules. Ensuring that certain data quality metrics are adhered to increases the quality of the data gathered. A few common metrics include accuracy, completeness, uniqueness, validity, consistency and linkage to relevant items.
• Advanced analytics engines: Cloud-based data analytics platforms can then be employed to run large-scale statistical analyses and build predictive models for customer behaviour. Such advanced engines can work on complex datasets and derive customer behaviour details. 
• Data storytelling dashboards: This is the process of translating data analyses results into understandable terms that can be used to influence a business decision or action. Cloud-based business intelligence (BI) tools like Tableau or Power BI can be employed to visualize insights and translate them into actionable strategies for improved customer experiences.

Big Data Analytics: Building customer-brand relationships & customer engagement

With the valuable insights derived from Big Data analytics, businesses gain significant customer insight that they can then use in everything from product research and development to marketing strategies and campaigns. The goal is to resonate with the customer and build an emotional relationship that will increase customer stickiness and brand loyalty.

Some of the most famous big data analytics success stories include Spotify which uses machine learning and artificial intelligence to offer personalized “Discover Weekly” playlists that recommend songs to users based on their song history. Another is Amazon, where Big Data helps them make better product recommendations to customers and improve the delivery experience with an intelligent logistics system that chooses the nearest warehouse.

It is clear that business success and the brand-customer relationship is more tightly linked than ever, which is why businesses need to invest in their Big Data collection and analytics to reap the most benefits – especially with an increasingly saturated marketplace in the digital era.

At Cloud Kinetics, we understand the value of intelligent data analytics. Our Data Engineering team has helped many companies collect, manage, and extract valuable insights from their data, enabling them to provide an improved customer experience and enjoy better business outcomes. Connect with us today to start your journey into Big Data analytics.

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