Integrating AI into Risk Management Frameworks

In my work with organizations rolling out AI and generative AI solutions, one concern I hear repeatedly from leaders, and the c-suite is how to get a clear, centralized “AI Risk Center” to track AI safety, large language model's accuracy, citation, attribution, performance and compliance etc. Operational leaders want automated governance reports—model cards, impact assessments, dashboards—so they can maintain trust with boards, customers, and regulators. Business stakeholders also need an operational risk view: one place to see AI risk and value across all units, so they know where to prioritize governance. One of such framework is MITRE’s ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Matrix. This framework extends MITRE ATT&CK principles to AI, Generative AI, and machine learning, giving us a structured way to identify, monitor, and mitigate threats specific to large language models. ATLAS addresses a range of vulnerabilities—prompt injection, data leakage, malicious code generation, and more—by mapping them to proven defensive techniques. It’s part of the broader AI safety ecosystem we rely on for robust risk management. On a practical level, I recommend pairing the ATLAS approach with comprehensive guardrails - such as: • AI Firewall & LLM Scanner to block jailbreak attempts, moderate content, and detect data leaks (optionally integrating with security posture management systems). • RAG Security for retrieval-augmented generation, ensuring knowledge bases are isolated and validated before LLM interaction. • Advanced Detection Methods—Statistical Outlier Detection, Consistency Checks, and Entity Verification—to catch data poisoning attacks early. • Align Scores to grade hallucinations and keep the model within acceptable bounds. • Agent Framework Hardening so that AI agents operate within clearly defined permissions. Given the rapid arrival of AI-focused legislation—like the EU AI Act, now defunct  Executive Order 14110 of October 30, 2023 (Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence) AI Act, and global standards (e.g., ISO/IEC 42001)—we face a “policy soup” that demands transparent, auditable processes. My biggest takeaway from the 2024 Credo AI Summit was that responsible AI governance isn’t just about technical controls: it’s about aligning with rapidly evolving global regulations and industry best practices to demonstrate “what good looks like.” Call to Action: For leaders implementing AI and generative AI solutions, start by mapping your AI workflows against MITRE’s ATLAS Matrix. Mapping the progression of the attack kill chain from left to right - combine that insight with strong guardrails, real-time scanning, and automated reporting to stay ahead of attacks, comply with emerging standards, and build trust across your organization. It’s a practical, proven way to secure your entire GenAI ecosystem—and a critical investment for any enterprise embracing AI.

AI Governance: Map, Measure and Manage 1. Governance Framework:   - Contextualization: Implement policies and practices to foster risk management in development cycles.   - Policies and Principles: Ensure generative applications comply with responsible AI, security, privacy, and data protection policies, updating them based on regulatory changes and stakeholder feedback.   - Pre-Trained Models: Review model information, capabilities, limitations, and manage risks.   - Stakeholder Coordination: Involve diverse internal and external stakeholders in policy and practice development.   - Documentation: Provide transparency materials to explain application capabilities, limitations, and responsible usage guidelines.   - Pre-Deployment Reviews: Conduct risk assessments pre-deployment and throughout the development cycle, with additional reviews for high-impact uses. 🎯Map 2. Risk Mapping:   - Critical Initial Step: Inform decisions on planning, mitigations, and application appropriateness.   - Impact Assessments: Identify potential risks and mitigations as per the Responsible AI Standard.   - Privacy and Security Reviews: Analyze privacy and security risks to inform risk mitigations.   - Red Teaming: Conduct in-depth risk analysis and identification of unknown risks. 🎯Measure 3. Risk Measurement:   - Metrics for Risks: Establish metrics to measure identified risks.   - Mitigation Performance Testing: Assess effectiveness of risk mitigations. 🎯Manage 4. Risk Management:   - Risk Mitigation: Manage risks at platform and application levels, with mechanisms for incident response and application rollback.   - Controlled Release: Deploy applications to limited users initially, followed by phased releases to ensure intended behavior.   - User Agency: Design applications to promote user agency, encouraging users to edit and verify AI outputs.   - Transparency: Disclose AI roles and label AI-generated content.   - Human Oversight: Enable users to review AI outputs and verify information.   - Content Risk Management: Incorporate content filters and processes to address problematic prompts.   - Ongoing Monitoring: Monitor performance and collect feedback to address issues.   - Defense in Depth: Implement controls at every layer, from platform to application level. Source: https://lnkd.in/eZ6HiUH8

Financial organizations struggle to predict the credit risk of millions of members. That’s because everyone has unique spending habits, plus economic conditions vary. This Azure-powered risk management architecture addresses these challenges and evaluates default probabilities: 1️⃣ Data Integration Collect and unify transaction histories and credit scores using Azure Data Lake Storage, processed via Data Factory, and analyzed with Synapse Analytics. 2️⃣ Data Preprocessing Clean, enrich, and prepare data with Azure Synapse and Data Factory for seamless analysis. 3️⃣ AI-Driven Model Development Build, train, and evaluate credit risk models in Azure Machine Learning, integrating feature engineering, fairness checks, and interpretability. 4️⃣ Flexible Deployment Deploy models on Managed Endpoints for both real-time and batch inference. 5️⃣ Real-Time and Batch Predictions Enable fast and accurate predictions through APIs or data pipelines, catering to diverse use cases. 6️⃣ Actionable Insights Visualize predictions and trends in Power BI, empowering smarter and transparent loan decisions. 7️⃣ End-to-End Reporting Generate detailed performance metrics with tools like Synapse Analytics and SQL for ongoing monitoring. The result? ⚡ Scalable credit risk assessments ⚡ More accurate default predictions ⚡ Fair and responsible loan decisions How do you see AI simplifying credit risk modeling? Let’s discuss! #Azure #AI #CreditRisk #FinTech #MachineLearning #DataAnalytics