AI in Risk Management: Lessons Learned

AI copilots and agents are flooding into governance, risk, and compliance. But beneath the hype, we’re already seeing failure patterns emerge: ❌ Hallucinated policies: AI inventing fake clauses that never existed in ISO, GDPR, or NIST. ❌ Fabricated audit evidence: tools “filling in” missing diagrams or logs to look complete. ❌ Sensitive data leakage: employees pasting confidential info into public LLMs, later showing up in training sets. ❌ Auditor rejection: polished AI-generated reports with no traceability don’t pass external audit. ❌ Overstepping autonomy: agentic bots firing off vendor warnings without human review, damaging relationships. ❌ Integration failures: broken data feeds causing false compliance alerts and fire drills. ❌ Compliance gaps: organizations skipping DPIAs or risk assessments for the AI itself, creating new violations. Lesson: AI doesn’t remove the need for strong GRC controls. It shifts them. You still need validation, approvals, documentation, and guardrails: just tailored to AI’s failure modes. #CyberRisk #AIGovernance #CISOInsights

Your AI project will succeed or fail before a single model is deployed. The critical decisions happen during vendor selection — especially in fintech where the consequences of poor implementation extend beyond wasted budgets to regulatory exposure and customer trust. Financial institutions have always excelled at vendor risk management. The difference with AI? The risks are less visible and the consequences more profound. After working on dozens of fintech AI implementations, I've identified four essential filters that determine success when internal AI capabilities are limited: 1️⃣ Integration Readiness For fintech specifically, look beyond the demo. Request documentation on how the vendor handles system integrations. The most advanced AI is worthless if it can't connect to your legacy infrastructure. 2️⃣ Interpretability and Governance Fit In financial services, "black box" AI is potentially non-compliant. Effective vendors should provide tiered explanations for different stakeholders, from technical teams to compliance officers to regulators. Ask for examples of model documentation specifically designed for financial service audits. 3️⃣ Capability Transfer Mechanics With 71% of companies reporting an AI skills gap, knowledge transfer becomes essential. Structure contracts with explicit "shadow-the-vendor" periods where your team works alongside implementation experts. The goal: independence without expertise gaps that create regulatory risks. 4️⃣ Road-Map Transparency and Exit Options Financial services move slower than technology. Ensure your vendor's development roadmap aligns with regulatory timelines and includes established processes for model updates that won't trigger new compliance reviews. Document clear exit rights that include data migration support. In regulated industries like fintech, vendor selection is your primary risk management strategy. The most successful implementations I've witnessed weren't led by AI experts, but by operational leaders who applied these filters systematically, documenting each requirement against specific regulatory and business needs. Successful AI implementation in regulated industries is fundamentally about process rigor before technical rigor. #fintech #ai #governance

NIST’s new Generative AI Profile under the AI Risk Management Framework is a must-read for anyone deploying GenAI in production. It brings structure to the chaos mapping GenAI-specific risks to NIST’s core functions: Govern, Map, Measure, and Manage. Key takeaways: • Covers 10 major risk areas including hallucinations, prompt injection, data leakage, model collapse, and misuse • Offers concrete practices across both open-source and proprietary models • Designed to bridge the gap between compliance, security, and product teams • Includes 60+ recommended actions across the AI lifecycle The report is especially useful for: • Organizations struggling to operationalize “AI governance” • Teams building with foundation models, including RAG and fine-tuned LLMs • CISOs and risk officers looking to align security controls to NIST standards What stood out: • Emphasis on pre-deployment evaluations and model monitoring • Clear controls for data provenance and synthetic content detection • The need for explicit human oversight in output decisioning One action item: Use this profile as a baseline audit tool evaluate how your GenAI workflows handle input validation, prompt safeguards, and post-output review. #NIST #GenerativeAI #AIrisk #AIRMF #AIgovernance #ResponsibleAI #ModelRisk #AIsafety #PromptInjection #AIsecurity

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.