Developing an AI Risk Management Culture

"this toolkit shows you how to identify, monitor and mitigate the ‘hidden’ behavioural and organisational risks associated with AI roll-outs. These are the unintended consequences that can arise from how well-intentioned people, teams and organisations interact with AI solutions. Who is this toolkit for? This toolkit is designed for individuals and teams responsible for implementing AI tools and services within organisations and those involved in AI governance. It is intended to be used once you have identified a clear business need for an AI tool and want to ensure that your tool is set up for success. If an AI solution has already been implemented within your organisation, you can use this toolkit to assess risks posed and design a holistic risk management approach. You can use the Mitigating Hidden AI Risks Toolkit to: • Assess the barriers your target users and organisation may experience to using your tool safely and responsibly • Pre-empt the behavioural and organisational risks that could emerge from scaling your AI tools • Develop robust risk management approaches and mitigation strategies to support users, teams and organisations to use your tool safely and responsibly • Design effective AI safety training programmes for your users • Monitor and evaluate the effectiveness of your risk mitigations to ensure you not only minimise risk, but maximise the positive impact of your tool for your organisation" A very practical guide to behavioural considerations in managing risk by Dr Moira Nicolson and others at the UK Cabinet Office, which builds on the MIT AI Risk Repository.

A lot of companies think they’re “safe” from AI compliance risks simply because they haven’t formally adopted AI. But that’s a dangerous assumption—and it’s already backfiring for some organizations. Here’s what’s really happening— Employees are quietly using ChatGPT, Claude, Gemini, and other tools to summarize customer data, rewrite client emails, or draft policy documents. In some cases, they’re even uploading sensitive files or legal content to get a “better” response. The organization may not have visibility into any of it. This is what’s called Shadow AI—unauthorized or unsanctioned use of AI tools by employees. Now, here’s what a #GRC professional needs to do about it: 1. Start with Discovery: Use internal surveys, browser activity logs (if available), or device-level monitoring to identify which teams are already using AI tools and for what purposes. No blame—just visibility. 2. Risk Categorization: Document the type of data being processed and match it to its sensitivity. Are they uploading PII? Legal content? Proprietary product info? If so, flag it. 3. Policy Design or Update: Draft an internal AI Use Policy. It doesn’t need to ban tools outright—but it should define: • What tools are approved • What types of data are prohibited • What employees need to do to request new tools 4. Communicate and Train: Employees need to understand not just what they can’t do, but why. Use plain examples to show how uploading files to a public AI model could violate privacy law, leak IP, or introduce bias into decisions. 5. Monitor and Adjust: Once you’ve rolled out your first version of the policy, revisit it every 60–90 days. This field is moving fast—and so should your governance. This can happen anywhere: in education, real estate, logistics, fintech, or nonprofits. You don’t need a team of AI engineers to start building good governance. You just need visibility, structure, and accountability. Let’s stop thinking of AI risk as something “only tech companies” deal with. Shadow AI is already in your workplace—you just haven’t looked yet.

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.

A New Path for Agile AI Governance To avoid the rigid pitfalls of past IT Enterprise Architecture governance, AI governance must be built for speed and business alignment. These principles create a framework that enables, rather than hinders, transformation: 1. Federated & Flexible Model: Replace central bottlenecks with a federated model. A small central team defines high-level principles, while business units handle implementation. This empowers teams closest to the data, ensuring both agility and accountability. 2. Embedded Governance: Integrate controls directly into the AI development lifecycle. This "governance-by-design" approach uses automated tools and clear guidelines for ethics and bias from the project's start, shifting from a final roadblock to a continuous process. 3. Risk-Based & Adaptive Approach: Tailor governance to the application's risk level. High-risk AI systems receive rigorous review, while low-risk applications are streamlined. This framework must be adaptive, evolving with new AI technologies and regulations. 4. Proactive Security Guardrails: Go beyond traditional security by implementing specific guardrails for unique AI vulnerabilities like model poisoning, data extraction attacks, and adversarial inputs. This involves securing the entire AI/ML pipeline—from data ingestion and training environments to deployment and continuous monitoring for anomalous behavior. 5. Collaborative Culture: Break down silos with cross-functional teams from legal, data science, engineering, and business units. AI ethics boards and continuous education foster shared ownership and responsible practices. 6. Focus on Business Value: Measure success by business outcomes, not just technical compliance. Demonstrating how good governance improves revenue, efficiency, and customer satisfaction is crucial for securing executive support. The Way Forward: Balancing Control & Innovation Effective AI governance balances robust control with rapid innovation. By learning from the past, enterprises can design a resilient framework with the right guardrails, empowering teams to harness AI's full potential and keep pace with business. How does your Enterprise handle AI governance?