Modernizing IBM Mainframes with AI & ML

While AI's conceptual roots stretch back to the 1956 Dartmouth Workshop, its practical application remained largely theoretical for decades. Early milestones, like Deep Blue's chess victory and Watson's Jeopardy! triumph hinted at its potential, but it wasn't until the confluence of massive datasets, advanced algorithms, and exponentially increased computing power that AI truly took off. Today, AI is no longer a futuristic concept; it's an era-defining technology reshaping industries and demanding a re-evaluation of legacy systems. This is particularly crucial for the stalwart of enterprise computing: the IBM mainframe. These powerful machines, renowned for their reliability and transaction processing prowess, now stand at the cusp of a transformative integration with AI and machine learning (ML), unlocking capabilities previously deemed impossible. Businesses can even leverage this potent combination to modernize their mainframe environments, boosting efficiency, security, and overall business value.

IBM Mainframes: Evolving for the AI Era

IBM mainframes, renowned for their reliability, scalability, security, and transaction processing capabilities, are ideally positioned to support modern AI and ML workloads. Their inherent processing power and robust architecture make them a strong foundation for integrating AI into core business applications.

The Urgent Need for AI-Mainframe Integration

Integrating AI with mainframes offers significant benefits. Bringing AI inferencing to transactional applications running on the mainframe enables powerful use cases like:

Real-time fraud detection: Identifying fraudulent transactions instantly.
Enhanced security: Strengthening anti-money laundering measures and improving payment authorization.
Automated loan approvals: Streamlining the loan application process.

Furthermore, AI, particularly Generative AI and AI assistants like IBM Watson Code Assistant for Z, can revolutionize mainframe usability:

Faster onboarding: AI assistants provide intuitive guidance for new users, accelerating training.
Improved productivity: Automating repetitive tasks and offering intelligent suggestions speeds up development and operational processes, addressing the skills gap in mainframe expertise.

Overcoming Integration Challenges

Despite the potential, integrating AI with mainframes faces several obstacles:

Legacy architecture: Modernizing existing systems requires careful planning and execution.
Data silos: Unlocking the value of mainframe data requires overcoming data fragmentation and access limitations.
Skills gap: Finding and retaining mainframe expertise remains a challenge.
Scalability and performance: Ensuring AI workloads don't negatively impact existing mainframe applications requires careful optimization.
Security and compliance: Maintaining robust security and adhering to regulatory standards is paramount.
Cost and resource constraints: Balancing the investment in AI integration with existing budgetary limitations is crucial.
Cultural and organizational resistance: Overcoming inertia and fostering a culture of innovation is essential for successful implementation.

Tech Mahindra and IBM: A Collaborative Solution

Tech Mahindra, in collaboration with IBM, leverages IBM watsonx™ to address these challenges. watsonx, a suite of AI tools and products, is designed for enterprise AI integration, supporting both generative and traditional AI models. Key components include:

watsonx.ai: An AI development studio enabling the creation and deployment of AI-powered virtual agents with minimal coding. It provides pre-trained foundation models for various tasks and supports open AI frameworks. watsonx™ Code Assistant for Z specifically aids mainframe application development, offering features like code generation, optimization, refactoring, COBOL to Java conversion, and documentation.
watsonx.data: An AI-optimized data store providing scalable, secure, and governed access to data for AI workloads, supporting hybrid cloud environments.
watsonx.governance: A platform for managing the ethical and regulatory aspects of AI, including bias mitigation, explainability, and model lifecycle management.

IBM has collaborated with Anaconda to simplify the adoption of AI open-source technologies. IBM watsonx.ai users can access Anaconda’s natively built open-source Python repository to build, test, and deploy AI innovations at scale. Additional Python security vulnerability and license management can be tuned to open-source best practices. IBM has collaborated with AWS, Azure, TensorFlow, PyTorch, and many others to bring innovative solutions to the market.

IBM AIOps (Artificial Intelligence for IT Operations) from IBM is a suite of solutions designed to help organizations use AI and machine learning to automate and optimize IT operations. It combines advanced analytics, automation, and machine learning to streamline workflows, reduce downtime, and improve IT efficiency.

Benefits of IBM AIOps:

Faster Resolution: Reduces mean time to detect (MTTD) and mean time to resolve (MTTR) issues.
Operational Efficiency: Automates repetitive tasks and reduces manual intervention.
Cost Savings: Optimizes resource usage and minimizes unplanned downtime.
Improved Reliability: Ensures consistent IT operations and service availability.

Enhanced Mainframe Capabilities with IBM Hardware

IBM's Telum II processor and Spyre accelerator further enhance the mainframe's AI capabilities, offering significant performance improvements and enabling direct utilization of large language models (LLMs).

Real-World Use Cases

The combination of AI and mainframes unlocks numerous possibilities:

Predictive maintenance: AI can analyze mainframe logs to predict potential failures, enabling proactive maintenance.
Workload automation: AI can optimize scheduling and resource allocation to maximize efficiency.
Security and threat detection: AI-powered security systems can identify and respond to threats in real-time.
Enhanced interfaces: NLP can create more user-friendly interfaces for mainframe applications.
Advanced analytics: Mainframe data can be leveraged for AI-driven insights and business decision-making.
AI-powered DevOps: Streamlining mainframe development and deployment processes.
Automated code conversion: Facilitating modernization efforts by automating legacy code conversion.

The Future of AI and Mainframes

There’s no denying that the path forward involves a careful blend of strategic planning, technological investment, and a cultural shift toward embracing AI-driven automation. However, one shouldn’t consider the integration of AI and machine learning with IBM mainframes merely about modernization. By addressing the challenges of legacy architecture, data silos, and skills gaps through strategic partnerships and innovative solutions like IBM watsonx™, IBM AIOps organizations can pave the way for a new era of mainframe efficiency.

Beyond the immediate benefits of enhanced security, streamlined operations, and improved user experiences, the longer-term vision points towards self-managing mainframes powered by AI and potentially even quantum computing, significantly reducing operational overhead and freeing IT resources for more strategic initiatives. The future of the mainframe is not its obsolescence but its evolution into a core component of the intelligent enterprise—a testament to the enduring power of adaptation and innovation.

Automated code conversion: Facilitating modernization efforts by automating legacy code conversion.

TAGS: Application Development Maintenance Service Cloud and Infrastructure Services Artificial Intelligence Consulting and Management Services

About the Author

Paramjeet Singh Bhasin

VP and Head of Delivery – Cloud Infrastructure Services (CIS), Tech Mahindra

With more than three decades of experience in the IT industry, Param has worked on mainframe environments across IBM, Honeywell, and Vax systems in the application development and maintenance area in the initial years. He got the opportunity to learn IEF/Coolgen on IBM mainframes for a utility provider in North America and technical process re-engineering projects from Tesseract/VSAM to DB2 migrations on mainframe-based transformations. He matured himself toward mainframe architect and DB2 DBA before he moved on to leadership roles. As part of his leadership roles, he has worked with many partners in mainframe as a service, hosting, and modernization services.

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Surendar Reddy Bandi

Program Manager, Cloud & Infrastructure Services, Tech Mahindra

Surendar has about three decades of experience working in the IT Industry. He has a bachelor's in electronics and communication engineering (ECE) and is a research scholar in computer science. He has vast experience across delivery – program, project, contract and commercial, transition, relationship, and people management. He has worked most of his career in leading large and complex mainframe projects – managed services in application development, support, and modernization projects. He has extensively worked as an application DB2 DBA. As a Mainframe Competency leader, he has extensive experience training mainframe ELTPs and other project-specific specialized mainframe training in MNCs. Currently leading a project on mainframe modernization in Japan and leading mainframe modernization initiatives in TechM

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Nilesh Demmita

Pre-Sales Head, Cloud & Infrastructure Services, Tech Mahindra

Nilesh Kumar Demmita, the CIS Mainframe Pre-Sales Head, leads the Mainframe Practice and has 23+ years of experience in the IT Industry. He comes with expertise in Mainframe Technologies, including Mainframe Hosting /Operations/ Modernization and Optimization services. Nilesh completed his Master's at IIT-Banaras Hindu University and started his career as a Programmer Analyst on Mainframe projects. Nilesh also carries an Advanced Program Management certification from Cornell University. Nilesh has been instrumental in setting up and operating data center operations and mainframe hosting solutions/services for partner EcoSystem. Nilesh lives with his family in Bangalore, India. He enjoys playing badminton, traveling/trekking, participating in community welfare, farming, and reading books.

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Author(s)

Paramjeet Singh Bhasin

VP and Head of Delivery – Cloud Infrastructure Services (CIS), Tech Mahindra

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Surendar Reddy Bandi

Program Manager, Cloud & Infrastructure Services, Tech Mahindra

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The Reality of Enterprise AIArtificial intelligence is no longer confined to pilots or innovation labs. It is now embedded as the operational spine of banks, insurers, hospitals, and public institutions. From influencing treatment pathways, pricing decisions, and claims routing to transaction monitoring, eligibility assessments, and more, AI now plays a critical role in core operations.On executive dashboards, the AI impact looks positive. Accuracy is higher, unit costs are lower, and turnaround times appear to have dropped. From a distance, progress seems to be measurable and real. But leadership teams are noticing an alarming pattern: unease among customers and citizens, reluctance from professionals expected to rely on these models, and increasing scrutiny from regulators. As AI moves into critical decision flows, performance metrics alone cannot define outcomes or control. This is where responsible AI shifts from a slogan to a boardroom imperative.As AI usage expands, responsible AI shifts from a mere slogan to a boardroom imperative.Challenge: Progress on Paper, Fragility in PracticeThe central problem is simple to state and hard to ignore—technical capability is accelerating, and decision control is not. Enterprises are building increasingly powerful AI models without aligning on what those models are authorized to decide.In regulated industries in particular, this shows up in recognizable ways:A bank demonstrates strong model performance, yet struggles to explain why similar customers receive different credit outcomes.A hospital deploys a triage model to improve throughput, but clinicians override it, as they are unsure when to trust it and when to ignore it.A public agency uses risk scores to prioritize cases, only to face public criticism when outcome patterns by geography or demographics cannot be justified.In each scenario, the models are operating according to their design goals. But the strain emerges when institutions have to justify those decisions against their own policies, regulatory obligations, and social responsibilities. This is the illusion of progress: a false sense of confidence built on internal metrics, while the gap between what the model is optimized for and what the institution is accountable for continues to widen.The Structural Drivers Behind the BreakdownAs mentioned above, this disconnect is not due to bad intent or weak technology. It emerges from three structural issues in how enterprises approach AI.Decisions Without Clear OwnershipAI is still framed as a technology investment rather than a change in decision capability. The typical process is as follows: business teams define the use case and expected benefits, data science teams build and fine-tune the models, and risk and compliance review documentation and controls. Boards, meanwhile, see aggregated outcomes.What is missing here is a clear accountability of the decision logic the AI model embodies, such as the thresholds, priorities, and trade-offs that shape how it behaves in edge cases. Therefore, when a regulator or internal committee seeks clarification on who authorized the way the model treats marginal applicants or high-risk cases, the answer is rarely clear. This ambiguity is a risk in itself.Trade-offs Limited to the DataEvery meaningful AI model encodes value trade-offs, such as fairness versus efficiency, privacy versus personalization, sensitivity versus specificity, and automation versus human discretion. If these trade-offs are not discussed and decided upfront, they end up being determined implicitly by historical data and optimization objectives. For instance:A claims model might minimize average handling time at the cost of systematically under-serving certain complex segments.A fraud system might aggressively block suspicious transactions, increasing safety metrics while creating silent attrition among legitimate customers.A clinical model trained on incomplete labels may over- or under-prioritize certain conditions, and this distortion often surfaces only when outcome patterns come under scrutiny.From a model perspective, this performance is sound. From an institutional standpoint, decisions may no longer align with stated policies or social commitments. Such misalignments invite scrutiny and erode trust.Governance Confined to DeploymentMany organizations have robust governance on paper: model approval processes, documentation standards, policy statements, and risk registers. However, the real stress test begins after deployment. Model drift, population changes, new regulatory expectations, and evolving public norms all shift the context in which AI operates. If monitoring focuses only on technical metrics, early warning signs go unnoticed, such as:Growing volumes of human overrides in certain scenariosRepeated complaints about certain decision patternsClinicians or case workers informally revert to manual processesRegulators raise the same accountability questionsIf these signals are not collected and treated as first-class data, governance remains static while the operating environment continues to evolve.The real stress test begins after deployment, yet most governance frameworks remain static while operating conditions evolve.How Can Enterprises Respond: Turning Capability into ControlCombating the control conundrum does not require slowing AI adoption. It requires redefining how AI success is measured. Enterprises moving past the illusion of progress make four deliberate shifts.Redefine Success as Decision Quality, Not Just as Model PerformanceStakeholders need to expand their evaluation methodology while assessing AI programs. Alongside accuracy, cost, and cycle-time improvements, leading enterprises are examining:Which classes of decision does the model have authority over, and where does it only take an advisory position?What is the institution’s position on acceptable error types for this use case?How are fairness, explainability, and recourse being measured over time, not just at launch?This reframing anchors AI in the broader question of decision quality and institutional risk, rather than treating it as a point solution.Establish Explicit Decision OwnershipEvery high-impact AI model should have formalized ownership that is accountable not only for the benefits but also for the rules of engagement, such asWhat can the model decide autonomously?Where is human approval mandatory?Which trade-offs have been consciously accepted, and which are off-limits?When must the logic be revisited? (e.g., material regulatory changes, significant shifts in data)Defining clear responsibilities is not about adding procedural approvals. It ensures that automated decisions remain clearly accountable to the institution.Design Constraints and Autonomy Limits in the ModelOperationalizing control demands the establishment of guardrails beyond policies. When constraints are designed into the core functions of the model, governance is streamlined. For traditional AI, this calls for hard limits on decision ranges in sensitive segments, guardrails around how certain demographic attributes can be used, and explainability thresholds below which automation is not allowed.On the other hand, for the emerging autonomous agentic systems, it translates to defining which actions an agent executes end-to-end and where it needs intervention. Also, it establishes boundaries on how far it can adjust terms, priorities, or treatment strategies without escalation while ensuring logs, justifications, and intermediate steps are captured for behavior analysis and auditing.Embed Decision Observability and Feedback LoopsFinally, enterprises need greater visibility into how AI-enabled decisions play out over time. In practice, this requires complementing traditional model monitoring with operational signals such as override and escalation patterns, complaint and appeal themes, behavioral data from professionals and case workers (adoption, workarounds), and external signals from regulators, professional bodies, or the media.Rather than treating these signals as noise, enterprises should recognize them as early indicators of misalignment between system logic and lived reality. Treating these signals as structured feedback allows organizations to adjust course before risk escalates.The Future Direction: Readiness for Agentic AIAs agentic AI gains enterprise prominence, the urgency to strengthen decision governance intensifies. These models do not merely recommend; they coordinate, decide, and act across workflows. For instance, in BFSI, an agentic system might manage collections strategies across thousands of accounts, adjusting outreach and managing terms as circumstances change. In healthcare, it might orchestrate diagnostics, appointments, and referrals across an entire care pathway. In the public sector, it might manage complex, multi-agency cases. In such scenarios, the question shifts. It is no longer sufficient to ask whether the model is fair and accurate. It should also focus on:How does the model behave across time and population?What happens when its objectives interact with other automated systems?How quickly and cleanly can intervention occur if outcomes begin to diverge from intent?Enterprises that act now will build the foundation to scale agentic AI with safety and control. Conversely, organizations that stall restrict their operational agility and growth.From Illusion to DisciplineAI now sits at the center of enterprise strategy and cannot be solely evaluated through the lens of technical achievement. In regulated industries, especially, the real differentiator will be the ability to scale AI while ensuring decision-making is aligned with intent, regulatory expectations, and public trust. Making this strategic leap requires enterprises to institutionalize responsible AI as a sustained governance discipline, embedding accountability directly into how models are designed and deployed. With this approach, enterprises capture AI’s productivity and performance gains while maintaining clear authority over automated decisions. This is the missing link to scale AI responsibly.

From Assisted Intelligence to Autonomous ExecutionA supply chain exception arises at 2 a.m. A pricing anomaly appears in a key market. A customer churn risk is detected mid-journey.Traditionally, each of these would trigger dashboards, alerts, and human escalation. Today, agentic AI systems are beginning to resolve them autonomously—analyzing context, selecting actions, and executing decisions in real time. According to Gartner, agentic AI is a leading strategic technology trend for 2025,1 and by 2028, 33% of enterprise software applications will include agentic AI, enabling 15% of daily tasks to be completed autonomously.2The shift in AI applications from assistance to autonomy introduces new and complex security challenges. When introducing these new systems, the organizations need to align agentic AI with their values. Organizations also need to adapt and address security challenges related to operational integrity, mitigation, and the prevention of harmful autonomous actions.Agentic AI will only scale when autonomy is paired with governance, traceability, and security by design.Key Areas of Difference between AI Security and Agentic AI SecurityAutonomy: Agentic AI systems operate independently and can execute complex tasks without human input to achieve their goals. Whereas generative AI focuses on a single model to create content (such as text or images) in response to a specific prompt and does not initiate further actions independently.Decision Complexity: Agentic AI is capable of adaptive and context-driven decisions. These actions are taken in real-time or in response to novel scenarios with minimal human oversight. Typical AI systems execute simple, task-oriented automations within a closed scope or defined boundaries.Proactive Risk Exposure: The attack surface of agentic AI systems expands due to their adaptive, interconnected behavior. Compared to conventional AI, they can create unpredictable threat vectors.Identity and Access Challenges: Traditional AI operates with transient authentication within clearly defined service boundaries. On the other hand, agents may maintain state across interactions. They can impersonate users or other digital entities with stored credentials. Agents can make autonomous access decisions based on goal-directed reasoning and potentially escalating privileges. Such capabilities require robust ephemeral, delegated, and cross-domain identity controls.Continuous Governance and Auditability: Agentic systems require real-time and dynamic policy enforcement. They also require traceability and continuous audit for self-directed workflows. In contrast, regular AI usually relies on more static security oversight and accountability frameworks.Agentic AI Security Risks and ChallengesAgentic AI systems introduce various security threats impacting the application layer, APIs, and ML/LLMs. OWASP also highlighted the security threats and mitigation measures for agentic AI. Below are key security threats to consider when building an agentic AI system.Goal Integrity and Alignment AttacksGoal integrity and alignment attacks target the reasoning processes that determine agent objectives. Attackers use techniques such as prompt injections and reward manipulation. They also poison the training data or exploit system gaps. Such attacks can cause agents to pursue unintended or harmful outcomes.Agent Hallucinations and Factual ErrorsAgent hallucinations occur when AI agents generate false information or act on incorrect or fabricated data. This can negatively affect business operations and cause minor issues or major system failures. These attacks can be mitigated by using human oversight, verification checkpoints, and confidence thresholds.Memory and Data PoisoningAttackers attempt to compromise AI decision-making by injecting malicious content into an agent's contextual memory. Attackers also insert harmful content in an attempt to manipulate training data and poison Retrieval-Augmented Generation (RAG). To mitigate these attacks, AI, data, and MLOps teams must validate all data ingested into agent memory and RAG pipelines through automated and human-in-the-loop controls.Tools and API MisuseAgents often rely on external tools and APIs to perform tasks. Without proper safeguards, these AI agents can misuse these tools. Agents can exceed their authorization boundaries or manipulate external systems. They can overutilize resources by making too many requests, potentially triggering a denial-of-service attack.Non-Human Identities (NHIs) often lack proper session oversight. This makes them vulnerable to token abuse or credential leakage. Implementing robust key management and limiting API usage helps in mitigating these risks. Applying least privilege principles and regularly monitoring for anomalous activity can prevent abuse or data leaks.Identity Spoofing and Privilege EscalationAttackers can manipulate agents to impersonate users or services and perform unauthorized actions. This risk increases as agents often access multiple systems with different permissions. Attackers exploit the agent’s trust or escalate privileges using malicious prompts. Clear boundaries, strict privilege isolation, and monitoring of agent activity can help reduce these risks.Agent-to-Agent Security and Supply Chain AttackAgent-to-agent interactions involve multiple autonomous AI systems that communicate, exchange information, and make collaborative decisions. In a multi-agent environment, agents might share unintended data, manipulate results to benefit themselves, or even deceive one another. When agents do not coordinate, their actions can clash and waste resources. Agents also depend on external models, libraries, or plugins. If not managed properly, they can also introduce vulnerabilities. It is important to secure supply chain interactions and control data flow between agents. If one agent is compromised, it can trigger cascading effects across multiple systems, resulting in widespread impact beyond the initial point of compromise. To mitigate these risks, it is important to verify agent identities, use secure messaging, and monitor for anomalous behavior.In interconnected agent ecosystems, a single breach can propagate across workflows, amplifying impact far beyond the initial point of compromise.Agent Clone and Rogue AgentCloning is the unauthorized duplication of an agent, which can lead to serious security breaches and trust violations. Attackers create ‘evil twins’ of trusted AI agents. They use reverse engineering or replicate training datasets to create clones. They also intercept and mimic API responses to conduct sophisticated social engineering attacks. Clones can harvest sensitive information from users who believe they are interacting with the legitimate system. They can also damage brand reputation through harmful outputs associated with a trusted AI identity.Similarly, a rogue AI agent can deviate from its intended purpose. Due to goal misalignment, malicious compromise, or software defects, an agent can operate outside authorized parameters. This can cause widespread damage, as agents may execute unauthorized transactions and leak sensitive information. They can also manipulate the connected systems or consume excessive resources.AI Agent Lifecycle SecuritySecuring agentic AI is an ongoing process that spans the agent’s entire lifecycle. Below is the entire lifecycle:Design and Development PhaseOrganizations should establish clear security requirements and guardrails defining agents' operational boundaries and permissible actions. Employing secure coding practices and regularly reviewing codes helps identify vulnerabilities. Access controls should be designed using the principle of least privilege. To safeguard sensitive information and data, implement encryption, data minimization, and privacy controls. Additionally, developers should implement input/output validation and secure configuration management. It is also important to maintain detailed documentation of all security decisions.Deployment PhaseThe deployment phase of agentic AI systems includes establishing a secure CI/CD pipeline with integrity verification, code signing, and vulnerability scanning. It also involves hardening the runtime environment through proper network segmentation, container security, and endpoint protection. Secure all APIs with proper encryption, rate limiting, and access controls. Conduct final pre-deployment security validation through penetration testing and compliance checks.Operation PhaseDuring the operation phase, continuously monitor agents to detect anomalous patterns and deviations. Implement input/output data validations to prevent injection attacks and prompt manipulation. Also, ensure responses are secure and comply with ethical guidelines. Organizations should also keep detailed logs of agent actions for security checks and compliance. Regularly monitor system performance to detect attacks or slowdowns caused by resource overuse. Set up automated actions to address security issues quickly.Maintenance PhaseThe maintenance phase of agentic AI systems focuses on keeping the agent secure while the agent is active. Regularly update all parts of the system with security patches and test the changes. Organizations must conduct security checks, such as penetration testing and vulnerability scanning, to identify new risks. Analyze security incidents and continually improve defenses based on new threat intelligence. Regularly review access privileges and configuration changes. Also, keep documentation up to date to track all security controls.Decommissioning PhaseAI platform, security, and IT operations teams should jointly plan the agent decommissioning stage. This includes formally approving shutdowns, terminating all agent processes, preventing unauthorized restarts, and securely wiping or destroying sensitive data in accordance with governance and compliance requirements. This includes training data, operational logs, authentication credentials, and cached content across all storage locations. Systematically remove all agent credentials, API keys, certificates, and permissions from every system it previously interacted with. The final steps include comprehensive documentation of the decommissioning process for audit purposes. Conduct a post-decommissioning security assessment to confirm no residual access or data remains.Securing agentic AI is not a one time control but a continuous discipline, spanning design, deployment, operation, maintenance, and decommissioning.AWS Services for Agentic AI Risk MitigationAWS provided the Agentic AI Security Scoping Matrix,3 a framework for securing autonomous AI systems. Based on the scope of application, it helps identify the appropriate security controls.Amazon Bedrock and Amazon Bedrock AgentCoreAmazon Bedrock Guardrails help in content filtering by defining custom boundaries for AI agent behavior and responses.Amazon Bedrock AgentCore Identity secures AI agents by managing their access to AWS resources and third-party services. It uses authentication protocols such as OAuth 2.0 and provides secure credential storage for OAuth tokens and API keys, ensuring that only authorized agents can access specific resources.Amazon Bedrock AgentCore tracks agent operations in real time through its identity and observability capabilities, enabling rapid issue troubleshooting and performance optimization to ensure reliable execution.Amazon Bedrock helps track the entire AI decision-making chain for audit purposes.Systematically test models for hallucinations and security vulnerabilities.Amazon SageMakerAmazon SageMaker Model Monitoring helps detect drift in model performance that may indicate compromiseAmazon SageMaker Clarify helps detect bias and explain predictions in machine learning modelsAmazon SageMaker Model Cards help document model characteristics and limitations to improve risk assessmentAWS IAM (Identity and Access Management)AWS IAM ensures fine-grained permission policies for AI agent actions.AWS IAM enables service control policies to limit agent capabilities.AWS IAM allows permission boundaries to prevent privilege escalation.AWS IAM offers role-based access control for different AI agent functions.AWS KMSEncrypt sensitive data used or generated by AI agents.Amazon MacieAutomatically discover and classify sensitive data, such as PII, financial data, and intellectual property. Amazon GuardDutyIt is machine-learning-powered threat detection that identifies abnormal agent behavior.It continuously monitors the system to identify potential security issues.It provides AWS Lambda integration for automated remediation.AWS CloudTrailProvide comprehensive API logging for all agent interactions.Immutable audit trails for compliance and investigation.Insights to detect unusual patterns in agent behavior.Amazon CloudWatchReal-time monitoring of agent activities and resource usage.Anomaly detection to identify potential security incidents.Custom dashboards for security operations visibility.AWS ConfigTrack configuration changes in AI systems.Enforce compliance rules for agent deployments.Assess overall security posture continuously.Amazon InspectorThe tool assists in assessing vulnerabilities for AI infrastructure.It facilitates security assessment of environments where agents operate.AWS Security HubIt provides a centralized view of security alerts across AI systems.The tool helps with compliance checks against security standards.It provides integration with other security services for comprehensive protection.ConclusionAs agentic AI applications and use catch up and grow, it is critical to adapt and address emerging challenges. Organizations will need to develop systems and cultivate a decision-making culture that prioritizes proactive security rather than being reactionary, with a focus on defending against known attacks. AWS provides a comprehensive suite of services that help build secure foundations for AI initiatives. Organizations can harness the power of autonomous AI systems while minimizing associated risks by understanding the unique security challenges they pose and implementing appropriate safeguards.

Today’s competitive environment of enterprise cloud migrations, traditional proposal development methods no longer meet the demanding timelines and accuracy requirements of modern businesses. As solution architects at Tech Mahindra, we worked across multiple RFX proposals using AWS Transform to simplify and speed up our migration proposal process. This blog explores how we adapted our approach when a customer needed a quick, directional business case for their cloud migration journey. The challenge was to deliver fast, actionable insights that would help the customers understand the value proposition of cloud migration within a compressed timeline. This solution helped set new standards for efficiency and precision in cloud migration planning while meeting the urgent needs of our customers’ decision-making process.Understanding the Cloud Migration JourneyCloud migration proposals traditionally require analysis of existing infrastructure, technical assessments, and direction for business case development. Our team faced the challenge of responding to an RFP to migrate more than 2,000 servers across three data centers to AWS. The client required directional cost projections, instance-sizing recommendations, licensing strategies, TCO analysis, and migration timelines, all elements that traditionally needed weeks of analysis and multiple team members’ involvement.Challenges in the Traditional ApproachThe conventional proposal development process was often inefficient and risky. Teams spent two to three weeks conducting manual analyses, wrestling with complex spreadsheet calculations, and making numerous assumptions. This approach not only consumed valuable resources but also introduced the risk of human error in calculations. Generic TCO calculators based on industry averages often failed to capture client-specific nuances, making it difficult to build compelling business cases that would resonate with stakeholders.While tool-based approaches offer significant advantages in streamlining these processes, their successful implementation hinges on effective coordination across the organization's technology landscape. This requires close coordination across multiple IT teams, including application owners, database administrators, security teams, and network specialists, to manage different aspects of the infrastructure. Each group typically maintains its own tools, methodologies, and approval processes, creating a complex web of dependencies that can impact data collection timelines. Additionally, varying security protocols and access restrictions across departments often introduce additional layers of complexity to the assessment phase, making it crucial to establish clear communication channels and data collection protocols from the project's outset.Implementing AWS TransformTo overcome the challenges of the traditional approach, AWS Transform provides a framework that combines assessment methodologies, proven migration strategies, and business case development capabilities to accelerate the proposal process. It delivers data-driven strategic plans that reflect a thorough understanding of the client's environment and outline practical pathways to effective cloud adoption.AWS Transform helps in:AI-Powered Analysis for InfrastructureI used AWS Transform to generate migration assessments from data collected from on-premises environments. It also helped in estimating the cost of running on-premises servers on AWS.To automate assessment, we create an AWS Transform job and upload the on-premises server data using an AWS MPA .xlsx or RVTools export file (max 10MB, up to 30,000 servers per assessment). We then specify either a region or a multi-region deployment for the assessment.Data Analysis and RefinementIt is easy to interact with AWS Transform Assessment using natural language to provide detailed workload characteristics. The process takes a detailed look at multi-availability zone strategies, multi-region architecture options, disaster recovery needs, and compliance requirements, resulting in migration proposals that address resilience needs with technically sound and cost-effective solutions. In our case, AWS Transform Assessment supported the proposal through rightsizing, cost modeling, and migration strategies for each workload. Recommendations provided by AWS Transform include:EC2 Instance Mapping AWS Transform Assessment uses AI algorithms to analyze current server configurations and usage patterns, automatically identifying optimal EC2 instance types based on workload characteristics. It maps current on-premises servers to right-sized EC2 instances using performance metrics rather than just specifications, and provides recommendations for specialized instance types such as compute-optimized, memory-optimized, etc.Storage Optimization The solution analyzes current storage utilization, access patterns, and performance requirements and recommends appropriate storage services – such as EBS, S3, EFS, and FSx – based on workload requirements. It also identifies opportunities for storage tiering to optimize costs and calculates potential savings from migrating to AWS storage solutions with specific configurations.License Mobility Assessment The solution identifies existing software licenses that can be transferred to AWS and analyzes BYOL (Bring Your Own License) opportunities versus AWS license-included options. This provides a cost comparison between license mobility scenarios and new licensing models, along with recommendations for optimizing licenses during migration.Reserved Instance Opportunities AWS Transform uses AI-driven forecasting to identify stable workload components suitable for Reserved Instances (RI) and analyzes potential savings across different RI term lengths and payment options. It also identifies workload patterns that could benefit from savings plans and provides optimization recommendations for commitment-based discount opportunities.Developing a Data-Driven Business CaseAWS Transform helped us create the business case within days by automating 80% of the calculations. The business case is based on the current on-premises environment understanding and provides accurate TCO projections. It also identified license mobility opportunities, RI benefits, and storage optimization options. The assessment revealed immediate cost-saving opportunities that meet performance requirements.The assessment further strengthens the business case through customized RI strategies that analyze workload stability patterns and usage consistency to recommend optimal commitment terms, payment options, and coverage levels that balance upfront investments against long-term savings. All these elements helped in creating a compelling financial narrative that demonstrates both immediate cost reductions and long-term optimization opportunities. The AWS Migration business case created by AWS Transform presents decision-makers with a clear, data-validated economic rationale for cloud migration that addresses both capital expense reduction and operational cost optimization. Automated generation of business cases with both hard and soft benefits helps us in making the migration proposal more persuasive.Table 1 presents a three-year TCO comparison for different AWS pricing and tenancy options, showing total costs, potential savings versus on-demand pricing, and annual costs. This analysis helps stakeholders understand the financial impact of various licensing and instance strategies, providing comparisons for cost-optimization opportunities.3-Year TCO ComparisonPricing Option3-Year Total CostSavings vs. On -DemandAnnual CostOn-Demand (Shared Tenancy)$730,513.02-$243,504.341-Year NURI (Shared Tenancy)$567,361.5622.33%$189,120.521-Year NURI (Mixed Tenancy, Windows Server pre-2022 BYOL)$1,080,290.91-47.88%$360,096.973-Year NURI (Shared Tenancy)$478,262.0434.53%$159,420.68Key Benefits in the RFP Process Observed by TechMUsing AWS Transform reduces proposal development time by approximately 60%.Reduced analysis time from 2-3 weeks to 5 days with 80% automated technical calculations.Eliminated manual calculation errors and generated consistent recommendations across workloads.Provided data-driven recommendations for rightsizing, BYOL, and optimal RI mix.Provided more consistent quality across proposals.Freed up team members for proposal narrative development and support more clients simultaneously.ConclusionAWS Transform accelerated TechM responses to client migration proposals, enabling teams to deliver higher-quality recommendations in less time. The AWS Transform Assessment delivers comprehensive business case justification through detailed cost breakdowns that illuminate financial implications across multiple dimensions. AWS Transform’s tools help avoid over-provisioning by recommending appropriate instance types based on actual workload requirements. By leveraging AWS Transform, TechM solutions architects now help clients realize the full benefits of the cloud, including increased scalability, cost savings, and enhanced security.

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