How to Manage AI Risk

"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.

As organizations transition from pilots to enterprise-wide deployment of Generative and Agentic AI, it's crucial to recognize that GAI risks differ significantly from traditional software risks. Towards that, it is important to go back to basics and this publication from 2024 by National Institute of Standards and Technology (NIST)'s Generative AI Profile does a great job! 🌐 Here are the four highest-impact risks and the mitigation actions every organization should implement:- 1. Systemic Risk: Algorithmic Monocultures & Ecosystem-Level Failures When multiple industries depend on the same foundation models, a single unexpected model behavior can lead to correlated failures across the ecosystem. ⚡ Mitigation: - - Build model diversity and avoid single-model dependencies. - Maintain fallback systems and contingency workflows. - Apply stress tests that simulate sector-wide shocks. 2. Human-Originating Risks (Misuse, Over-Trust, Manipulation) Many GAI incidents stem from human behavior, including misuse, over-reliance, indirect prompt injection, and flawed assumptions. ⚡ Mitigation:- - Implement continuous user education on limitations and safe use. - Enforce access controls, privilege separation, and plugin vetting. - Maintain audit trails and logging to identify misuse early. 3. Content Integrity Risks (Hallucinations, Synthetic Media, Provenance Failure) GAI increases the scale and believability of fabricated content, from medical misinformation to deepfake-enabled harms. ⚡ Mitigation:- - Invest in content provenance, watermarking, and metadata tracking. - Require pre-deployment testing for hallucination profiles across contexts. - Use cross-model verification before high-stakes outputs are acted upon. 4. Security Risks (Prompt Injection, Data Leakage, Model Extraction) NIST highlights increasingly sophisticated attack surfaces unique to LLMs: indirect prompt injection, data extraction, and plugin-initiated compromise. ⚡ Mitigation:- - Apply secure-by-design reviews for all LLM integration points. - Red-team regularly using GAI-specific attack methods. - Log inputs/outputs via incident-ready documentation so breaches can be traced. 🔐 The bottom line:- AI risk management is not a technical afterthought, it is now a core capability. Organizations that operationalize governance, provenance, testing, and incident disclosure (NIST’s four focus pillars) will be the ones that deploy AI safely and at scale. 💬 If you’d like to explore Gen AI and Agentic AI risks, practical mitigation strategies, or how to operationalize the NIST AI RMF for your organization, feel free to comment or DM. Let’s build safer AI systems together! #AI #GenAI #AIGovernance #NIST #AIRMF #RiskManagement #AITrust #ResponsibleAI #AILeadership

Everyone is shouting “AI IS A BUBBLE!” But inside real companies, this is what is already happening today: —>Your Salesforce , monday.com , Workday , GitHub all quietly ship AI features. —>Your data lake is plugged into open source models to speed up internal workflows. —>Your employees run BYOA, Bring Your Own AI, to hit targets faster. This is not a bubble. This is production. And AI #security is not part of the “bubble”, it is part of survival. So if you already use #AI and want to scale it, here is a simple readiness checklist I would do: 1. #DLP for AI Risk: People paste secrets into prompts. Models remember. Data leaks. Action: Decide what data is “never for AI”, then enforce it at browser, API, and chat level. Not in a PDF, in the workflow. 2. #Threat intelligence for AI Risk:Prompt injection and model abuse are new attack paths, your old TI feeds do not see them. Action: Track AI specific indicators, jailbreak patterns, and tool abuse, and plug them into SOC playbooks and detections. 3. Supply chain and #models Risk: You rely on vendors, plugins, open source models, and datasets you barely review. Action: Treat every model and AI vendor as third party risk, run a review, keep versions and SBOM where you can, block “shadow AI tools”. 4. #Privacy Risk: PII flows into training, logs, and analytics without clear rules. Action: Map where personal data touches AI, set strict retention and minimization, and design prompts and systems to avoid PII by default. 5. #Identity and access in an agentic world Risk: Agents act on behalf of users with over privileged keys, no clear “who did what”. Action: Give agents their own scoped identities, least privilege per tool, full audit trail, and approvals for high risk steps. 6. AI operations and #governance Risk: Every team experiments; nobody owns the risk. Until something breaks. Action: Create a small AI security and governance group, keep an AI risk register, and review new AI use cases before they hit production. You do not need a 100 page framework to start. Pick one line from this list, fix it this quarter, then move to the next.

🔴 Your organization has an AI governance problem. You just don't know it yet. Here's what's actually happening right now: Your people are using AI tools you didn't approve. Your data is flowing into third-party systems you can't audit. Your automated processes are making decisions you can't reverse. And accountability for all of it? Distributed across a committee no one chairs. That's not a technology problem. That's a leadership failure. The CARE framework—Catastrophize, Assess, Regulate, Exit—gives you a way out. Five moves, starting today: 🔸 1. Ask your direct reports what AI they're actually using. Not what you authorized. What they're using. 🔸 2. Map your irreversibility points. Where does an AI failure become unrecoverable before you even know it happened? 🔸 3. Classify your data. Every AI tool is a pipeline running in both directions. Once proprietary data enters a third-party system, you can't get it back. 🔸 4. Red team for misuse—not just malfunction. "What if this breaks?" is the easy question. "What if this works perfectly and someone points it the wrong direction?" That's the one that keeps you up at night. 🔸 5. Name one executive who owns AI risk. Full stop. Authority, budget, board access. Not a committee. A person. Governance isn't the enemy of AI adoption. Ungoverned AI is. What's your organization actually doing about shadow AI exposure? 📍 🔗 Find out more in our new Fast Company article here: https://lnkd.in/gfBSFPFA.

🚀 Launching Responsible AI: Your Guide to ISO/IEC 42001 Implementation The new ISO/IEC 42001:2023 standard is a game-changer, providing the first internationally recognized framework for an Artificial Intelligence Management System (AIMS). Implementing it isn't just compliance—it's about building trustworthy, ethical, and sustainable AI. Here is the 4-Phase Roadmap for achieving ISO 42001 certification and managing AI risks effectively: 1. Plan & Scope (Context & Leadership) Define Your Context: Understand the internal and external factors influencing your AI use. Establish Scope: Clearly define which AI systems and processes fall under the AIMS. Secure Commitment: Top management must publish an AI Policy and assign clear roles. 2. Risk Assessment & Planning Identify Unique Risks: Go beyond security to assess risks like bias, discrimination, lack of transparency, and potential harm. Set Objectives: Establish measurable AI objectives aligned with business goals and ethical principles. Select Controls: Produce a Statement of Applicability (SoA), choosing controls from Annex A (the 39 AI-specific controls). 3. Support & Operation Resource Allocation: Ensure adequate resources, infrastructure, and staff competence are in place. Operationalize the Lifecycle: Implement robust processes for the entire AI system lifecycle (design, development, testing, monitoring, and retirement). Mandate AIIAs: Conduct AI System Impact Assessments (AIIAs) to evaluate socio-technical risks before deployment. 4. Performance & Improvement (PDCA Cycle) Monitor and Measure: Continuously track the performance of the AIMS against objectives and controls. Audit Regularly: Conduct Internal Audits to ensure conformity and effectiveness. Continual Improvement: Use audit results and management reviews to iteratively enhance the AIMS, ensuring your framework adapts to evolving AI technologies and regulations. Why this matters: ISO 42001 provides the structure needed to move from vague ethical principles to concrete, auditable practices. It's the key to responsible AI governance. #ISO42001 #AIMS #ArtificialIntelligence #AIGovernance #RiskManagement #Compliance

The Cybersecurity and Infrastructure Security Agency (CISA), together with other organizations, published "Principles for the Secure Integration of Artificial Intelligence in Operational Technology (OT)," providing a comprehensive framework for critical infrastructure operators evaluating or deploying AI within industrial environments. This guidance outlines four key principles to leverage the benefits of AI in OT systems while reducing risk: 1. Understand the unique risks and potential impacts of AI integration into OT environments, the importance of educating personnel on these risks, and the secure AI development lifecycle.  2. Assess the specific business case for AI use in OT environments and manage OT data security risks, the role of vendors, and the immediate and long-term challenges of AI integration 3. Implement robust governance mechanisms, integrate AI into existing security frameworks, continuously test and evaluate AI models, and consider regulatory compliance.  4. Implement oversight mechanisms to ensure the safe operation and cybersecurity of AI-enabled OT systems, maintain transparency, and integrate AI into incident response plans. The guidance recommends addressing AI-related risks in OT environments by: • Conducting a rigorous pre-deployment assessment. • Applying AI-aware threat modeling that includes adversarial attacks, model manipulation, data poisoning, and exploitation of AI-enabled features. • Strengthening data governance by protecting training and operational data, controlling access, validating data quality, and preventing exposure of sensitive engineering information. • Testing AI systems in non-production environments using hardware-in-the-loop setups, realistic scenarios, and safety-critical edge cases before deployment. • Implementing continuous monitoring of AI performance, outputs, anomalies, and model drift, with the ability to trace decisions and audit system behavior. • Maintaining human oversight through defined operator roles, escalation paths, and controls to verify AI outputs and override automated actions when needed. • Establishing safe-failure and fallback mechanisms that allow systems to revert to manual control or conventional automation during errors, abnormal behavior, or cyber incidents. • Integrating AI into existing cybersecurity and functional safety processes, ensuring alignment with risk assessments, change management, and incident response procedures. • Requiring vendor transparency on embedded AI components, data usage, model behavior, update cycles, cybersecurity protections, and conditions for disabling AI capabilities. • Implementing lifecycle management practices such as periodic risk reviews, model re-evaluation, patching, retraining, and re-testing as systems evolve or operating environments change.

If your team is asking “Can we use this AI tool?” You need governance.   Especially when AI systems can develop discriminatory bias, give incorrect advice, leak customer data, introduce security flaws, and perpetuate outdated assumptions about users.   AI governance programs and assessments are no longer an optional best practice.   They're on the fast track to becoming mandatory as several AI regulations roll out. Most notably for high-risk AI use. I recommend AI assessments beyond high risk use cases to also capture the privacy, security and ethical risks. Here’s how companies can conduct an AI risk assessment: ✔ Start by building an AI data inventory List every AI tool in use, including hidden ones embedded inside vendor software. Capture data inputs, decisions it makes, who has access, and outputs. ✔ Assess the decision impact Identify where wrong AI decisions could cause harm or discriminate, and review AI systems thoroughly to understand if it involves high-risk.   ✔ Examine company data sources Check whether your training data is current, representative, and free from historical bias. Confirm you have disclosures and permissions for use. ✔ Test for bias and fairness Run scenarios through AI systems with different demographic inputs and look for discrepancies in outcomes. ✔ Document everything Maintain detailed records of the assessment process, findings, and changes you make. Regulations like the EU AI Act and the Colorado AI Act have specific requirements for documenting high-risk AI usage.   ✔ Build monitoring checkpoints Set regular reviews and repeat risk assessments when new products or services are introduced or as models, vendors, business needs, or regulations change. AI oversight isn’t coming someday. It’s here.   Companies that start preparing now will be ready when the new regulations come into force. Read our full blog for more tips and to see how to put this into action 👇

This document serves as a comprehensive workbook for addressing AI safety within public sector projects. 1️⃣ It introduces four key objectives for ensuring AI safety: performance, reliability, security, and robustness, emphasizing their importance in real-world AI applications. 2️⃣ Activities and strategies outlined help assess risks, ensure technical integrity, and mitigate failures throughout the AI project lifecycle, including design, development, and deployment stages. 3️⃣ The workbook emphasizes stakeholder engagement, iterative testing, and documentation as critical practices for managing AI safety. 4️⃣ Specific risks like data poisoning, adversarial attacks, model drift, overfitting, and non-deterministic behaviors are highlighted, with mitigation strategies tailored to each risk type. 5️⃣ Tools such as safety self-assessments and risk management plans are provided to embed safety assurance into ongoing AI operations. 6️⃣ The document also serves as a resource for training civil servants and technical teams on applying these principles effectively in public sector contexts. ✍🏻 David Leslie, Cami Rincón, Morgan Briggs, Antonella Maia Perini, Smera Jayadeva, Ann Borda, SJ Bennett, Christopher Burr, Claudia Fischer. AI Safety in Practice. The Alan Turing Institute. 2024.

⚠️Privacy Risks in AI Management: Lessons from Italy’s DeepSeek Ban⚠️ Italy’s recent ban on #DeepSeek over privacy concerns underscores the need for organizations to integrate stronger data protection measures into their AI Management System (#AIMS), AI Impact Assessment (#AIIA), and AI Risk Assessment (#AIRA). Ensuring compliance with #ISO42001, #ISO42005 (DIS), #ISO23894, and #ISO27701 (DIS) guidelines is now more material than ever. 1. Strengthening AI Management Systems (AIMS) with Privacy Controls 🔑Key Considerations: 🔸ISO 42001 Clause 6.1.2 (AI Risk Assessment): Organizations must integrate privacy risk evaluations into their AI management framework. 🔸ISO 42001 Clause 6.1.4 (AI System Impact Assessment): Requires assessing AI system risks, including personal data exposure and third-party data handling. 🔸ISO 27701 Clause 5.2 (Privacy Policy): Calls for explicit privacy commitments in AI policies to ensure alignment with global data protection laws. 🪛Implementation Example: Establish an AI Data Protection Policy that incorporates ISO27701 guidelines and explicitly defines how AI models handle user data. 2. Enhancing AI Impact Assessments (AIIA) to Address Privacy Risks 🔑Key Considerations: 🔸ISO 42005 Clause 4.7 (Sensitive Use & Impact Thresholds): Mandates defining thresholds for AI systems handling personal data. 🔸ISO 42005 Clause 5.8 (Potential AI System Harms & Benefits): Identifies risks of data misuse, profiling, and unauthorized access. 🔸ISO 27701 Clause A.1.2.6 (Privacy Impact Assessment): Requires documenting how AI systems process personally identifiable information (#PII). 🪛 Implementation Example: Conduct a Privacy Impact Assessment (#PIA) during AI system design to evaluate data collection, retention policies, and user consent mechanisms. 3. Integrating AI Risk Assessments (AIRA) to Mitigate Regulatory Exposure 🔑Key Considerations: 🔸ISO 23894 Clause 6.4.2 (Risk Identification): Calls for AI models to identify and mitigate privacy risks tied to automated decision-making. 🔸ISO 23894 Clause 6.4.4 (Risk Evaluation): Evaluates the consequences of noncompliance with regulations like #GDPR. 🔸ISO 27701 Clause A.1.3.7 (Access, Correction, & Erasure): Ensures AI systems respect user rights to modify or delete their data. 🪛 Implementation Example: Establish compliance audits that review AI data handling practices against evolving regulatory standards. ➡️ Final Thoughts: Governance Can’t Wait The DeepSeek ban is a clear warning that privacy safeguards in AIMS, AIIA, and AIRA aren’t optional. They’re essential for regulatory compliance, stakeholder trust, and business resilience. 🔑 Key actions: ◻️Adopt AI privacy and governance frameworks (ISO42001 & 27701). ◻️Conduct AI impact assessments to preempt regulatory concerns (ISO 42005). ◻️Align risk assessments with global privacy laws (ISO23894 & 27701).   Privacy-first AI shouldn't be seen just as a cost of doing business, it’s actually your new competitive advantage.