The Importance of AI in Risk Governance

🧩 AI Risk Oversight: Connecting Compliance, Strategy, and Board Responsibilities🧩 Corporate boards have a duty to align all initiatives, including those involving AI, with the organization’s mission, financial health, and enterprise risk management. While AI offers significant opportunities, its risks demand careful governance. Directors must move beyond compliance-driven oversight to adopt a strategic, integrated approach that safeguards organizational priorities. ➡️Linking AI to Mission and Values AI systems can amplify your organization’s mission by driving efficiency, improving decision-making, and creating value for your stakeholders, but poorly governed AI can do just the opposite. For example: 🔹AI missteps, like biased decision-making, can damage reputations and undermine commitments to fairness and inclusivity. 🔹A lack of oversight may lead to AI systems failing to serve the organization’s core purpose or violating stakeholder expectations. Boards can ensure alignment by embedding ethical AI principles, such as those found in #ISO42001, into governance frameworks. ➡️AI’s Financial Implications AI impacts the bottom line through potential cost savings, revenue generation, and risk exposure. Boards must weigh: 🔹Cost Savings: Automation and data-driven insights can reduce inefficiencies and improve margins. 🔹Revenue Opportunities: New products and services powered by AI can create competitive advantages. 🔹Risk Management: Financial losses due to AI failures, regulatory penalties, or legal actions from misuse can be significant. Tools like #ISO42005 (DIS) can help you assess and mitigate risks, enabling informed decisions that protect financial interests while maximizing returns. ➡️Managing AI within Enterprise Risk Frameworks AI introduces new dimensions of enterprise risk. You must integrate AI governance into the broader enterprise risk management strategy, considering risks like: 🔹Operational Disruptions: Failures in AI systems can impact core operations or supply chains. 🔹Regulatory Compliance: Laws governing AI are evolving, and non-compliance could lead to penalties. 🔹Reputational Risk: Public trust can erode if AI systems are perceived as unfair, opaque, or harmful. Standards like #ISO23894 provide actionable guidance for managing AI risks throughout its lifecycle, aligning with existing enterprise risk frameworks. ➡️A Balanced Approach: AI Oversight as a Strategic Imperative Boards must ensure AI strategies align with mission goals, drive financial performance, and mitigate enterprise risks. A balanced approach includes: 🔹Adopting Standards: Use #ISO42001 to establish an AI management system (#AIMS) and ISO42005 (DIS) to assess potential impacts. 🔹Prioritizing Risks: Leverage ISO23894 to identify and address AI-specific risks effectively. 🔹Integrating Oversight: Embed AI governance into broader strategic and risk discussions to ensure alignment with the organization’s mission. A-LIGN #TheBusinessofCompliance

𝐀𝐈 𝐫𝐢𝐬𝐤 𝐢𝐬𝐧’𝐭 𝐨𝐧𝐞 𝐭𝐡𝐢𝐧𝐠. 𝐈𝐭’𝐬 𝟏,𝟔𝟎𝟎 𝐭𝐡𝐢𝐧𝐠𝐬. That’s not hyperbole. A new meta-review compiled over 1,600 distinct AI risks from 65 frameworks and surfaced a tough truth: most organizations are underestimating both the scope and structure of AI risk. It’s not just about bias, fairness, or hallucination. Risks emerge at different stages, from different actors, with different incentives: • Pre-deployment design decisions • Post-deployment human misuse • Model failure, misalignment, drift • Unclear accountability across teams The taxonomy distinguishes between human and AI causes, intentional and unintentional behaviors, and domain-specific vs. systemic risks. But here’s the real insight: Most AI risks don’t stem from malicious design. They emerge from fragmented ownership and unmanaged complexity. No single team sees the whole picture. Governance lives in compliance. Development lives in product. Monitoring lives in infra. And no one owns the handoffs. → Strategic takeaway: You don’t need another checklist. You need a cross-functional risk architecture. One that maps responsibility, observability, and escalation paths, before the headlines do it for you. AI systems won’t fail in one place. They’ll fail at the intersections. 𝐓𝐫𝐞𝐚𝐭 𝐀𝐈 𝐫𝐢𝐬𝐤 𝐚𝐬 𝐚 𝐜𝐡𝐞𝐜𝐤𝐛𝐨𝐱, 𝐚𝐧𝐝 𝐢𝐭 𝐰𝐢𝐥𝐥 𝐬𝐡𝐨𝐰 𝐮𝐩 𝐥𝐚𝐭𝐞𝐫 𝐚𝐬 𝐚 𝐡𝐞𝐚𝐝𝐥𝐢𝐧𝐞.

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

America's new AI Action Plan, released yesterday by the White House, signals a clear shift toward deregulation and industry self-determination. It is key to recognize that many Federal barriers have been removed, thereby placing greater responsibility and accountability on businesses for good AI governance. For me, there are three critical implications for those committed to responsible AI and sound enterprise risk management: 1. The Governance Gap has Widened: The AI Action Plan emphasizes more speed with fewer safeguards, but market forces continue to demand responsible AI. It is vital for companies to, therefore, build strong internal governance frameworks otherwise they may find themselves exposed when incidents occur. 2. Risk Transfer, Not Risk Reduction: Removing regulatory controls and "red tape" doesn't eliminate AI risks, rather, it shifts liability to corporate decision-making. Ensure that your risk management frameworks are adequately prepared for this responsibility. 3. Competitive Advantage Through Ethics: Many of your competitors are racing to deploy AI quickly, but it is those organizations with mature ethical frameworks and risk management practices that will differentiate themselves over the long run with responsible innovation. America needs to step up and lead in AI; this will require American businesses to be prepared to lead responsibly without extensive federal guidance. How is your organization preparing for this shift from regulated compliance to voluntary governance? #AIforGood https://lnkd.in/es3bPckD