Steps to Develop a Multi-Lens Risk Framework

🚨🧠 AI Model Risk Management is not a slide deck. It’s an operating system for trust. Most organizations still treat AI risk like a one-time approval step: review the model, sign off, deploy, move on. That approach breaks quickly. Because model risk is not just about “bad outputs.” It includes data quality issues, bias, misuse, performance drift, security weaknesses, legal exposure, and changing real-world conditions after deployment. What I like about this AI Model Risk Management Framework is that it turns “Responsible AI” into something more practical: documentation + assessment + continuous feedback. The 4 pillars that make the framework useful 1)  Model Cards These document the model’s purpose, training data, capabilities, limitations, performance, and even adversarial resistance where possible. In plain terms: Know what you deployed, what it’s good at, and where it can fail. 2)  Data Sheets These describe the datasets behind the model: how data was created, what it contains, intended uses, potential biases, limitations, and ethical considerations. In other words: Know what shaped the model before you trust what it says. 3)  Risk Cards These summarize the key risks tied to the model, including observed issues, categories of harm, current remediations, and expected user behavior. This is where vague concerns become something operational. 4)  Scenario Planning This explores “what if?” situations: what if the model is misused what if it fails in an unexpected context what if bias, misinformation, or security issues show up in production That’s where resilience gets built. Why this framework matters The strongest idea in the document is that these four components are not separate checkboxes. They create a feedback loop: Model Cards inform risk understanding Data Sheets add context on strengths and weaknesses Risk Cards shape scenario planning Scenario planning feeds back into mitigation and documentation That loop is what turns AI governance from paperwork into practice. The bigger lesson If your AI model caused a serious incident tomorrow, could your team answer: What data trained it? What risks were already known? What scenarios were tested? What controls were put in place? What changed since deployment? If not, you may have an AI system in production — but not an AI risk program. 💬 Curious: Which part do you think most organizations skip first? Model Cards, Data Sheets, Risk Cards, or Scenario Planning? #AISecurity #ModelRiskManagement #ResponsibleAI #RiskManagement #AIGovernance #LLMSecurity #SecurityArchitecture #GRC #CyberSecurity #GenAI

Safety is not a theory problem. It is a user problem. I read Google’s Frontier Safety Framework 3.0 and pulled out what actually matters for builders and researchers. ↳ What to prioritize: ➤ Define critical capability levels across three risk families: misuse, ML R&D acceleration, misalignment ➤ Apply two layers before release: weight security to prevent leaks, plus a deployment safety case that proves mitigations work ➤ Watch the triggers: acceleration and automation of AI research demand stronger controls and stricter review ➤ Treat misalignment work as early stage: start by monitoring instrumental reasoning in high-stakes internal use and keep iterating ➤ Update on a cadence: treat this as a living system, not a one-time policy ↳ What this means for UX and product: ➤ Design the safety case into the interface: log evidence, evals, and red-team flows so reviewers can see risk reduction ➤ Make the control loop visible: sense, plan, act, reflect should be inspectable, interruptible, and reversible by humans ➤ Fail gracefully for users: hide latency with clear preambles, hand off to humans fast, explain what the model can and cannot do ➤ Standardize patterns: capability labels, identity checks, tool-use confirmations, and recovery paths across surfaces ➤ Measure real outcomes: fewer repeats, faster resolution, lower risk exposure, not just benchmark wins ↳ How I would apply this with my MASTER framework: ➤ Map workflows and stakeholders touching frontier models ➤ Audit readiness across data, identity, logging, and incident response ➤ Scan tools for evals, monitoring, and weight security ➤ Trial small experiments with explicit exit criteria ➤ Embed controls into operations and support ➤ Repeat and scale only when the safety case holds Useful over shiny, always. If your model is powerful enough to help, it is powerful enough to harm. Design for both. Follow me for human-centered AI, agent safety, and UX that ships responsibly. Re-share with one teammate who needs this lens. P.S. What part of your current UI would you turn into evidence for a safety case first?

AI Governance Frameworks Series (Post 8) 🏢 Bringing It All Together — Building an Enterprise AI Governance Program We’ve explored: ▪️ Ethical foundations (OECD) ▪️ Risk frameworks (NIST AI RMF) ▪️ Regulation (EU AI Act) ▪️ Management systems (ISO/IEC 42001) ▪️ Assurance & testing (UK) ▪️ Operational execution (Singapore) 📊 Now the big question: How do organizations combine all of this into one coherent AI Governance program? 🧭 Step 1: Establish AI Governance Leadership AI governance must start at the top. This includes: ▪️ Executive sponsorship ▪️ Defined AI accountability ▪️ Cross-functional oversight (Legal, Risk, Security, IT, Compliance, Data) ▪️ Clear AI policy and governance charter Without leadership alignment, AI governance becomes fragmented. 🔍 Step 2: Identify & Classify AI Use Cases Create an AI inventory: ▪️ Where is AI being used? ▪️ Is it internally developed or third-party? ▪️ Does it impact customers or employees? ▪️ Does it make automated decisions? Then classify AI systems by risk level: ▪️ Low impact ▪️ Medium impact ▪️ High impact ▪️ Regulated / high-risk You can align this step with NIST AI RMF or EU AI Act risk categories. 🛡️ Step 3: Conduct AI Risk & Impact Assessments For each material AI system, evaluate: ▪️ Bias & fairness risk ▪️ Privacy impact ▪️ Security vulnerabilities ▪️ Operational risk ▪️ Reputational exposure ▪️ Regulatory implications This is where risk management and governance intersect. ⚙️ Step 4: Implement Controls & Oversight Controls may include: ▪️ Human review processes ▪️ Data quality validation ▪️ Model monitoring & drift detection ▪️ Logging and documentation ▪️ Explainability requirements ▪️ Incident response procedures for AI failures This is where ISO 42001 becomes powerful — it operationalizes governance. 📊 Step 5: Monitor, Assure & Improve AI governance is not one-and-done. You need: ▪️ Ongoing monitoring ▪️ Independent validation ▪️ Internal audits ▪️ Performance reviews ▪️ Clear reporting to leadership This aligns closely with the UK AI Assurance model. 🔥 The Reality AI governance is not a single framework. It’s a layered ecosystem: Ethics → Risk → Regulation → Management System → Assurance → Continuous Improvement Organizations that integrate all layers build trustworthy, scalable, defensible AI programs. #AIGovernance #ResponsibleAI #AIRiskManagement #AICompliance #AIProgram #DigitalTrust #ArtificialIntelligence #Governance #TechRisk #GRC

If you’re: • Entering a new market with an existing service • Launching a new offering • Capturing market share during times of disruption Here are the 3 lenses I review with clients in order to make strategic decisions on where to invest time, energy, personnel and money to grow their company Lens 1: Historical Context & Qualification Here’s what to look for:  • What measurable, fact only (no opinion) data points can validate this opportunity’s viability? • Does the new market fit your criteria (and align with your capabilities)? • What’s the risk vs. upside and how do we quantify both? This can help you easily eliminate bad ideas you thought were opportunities. Lens 2: The Future Since no one can predict the future, you have to go further than just reviewing market trends and forecast reports (which are valuable in themselves) Here you must identify first, second and third order consequences of what might potentially happen. As you review each potential consequence, you have to process how that may affect you. A simple way to do this is by asking “if this were true, how would this affect X” Since we’re dealing with hypotheticals, we’ll tie this thinking to Lens 3 to keep it grounded. Lens 3: Unchanging Principles Principles are laws or foundational blocks that never change regardless of the situation. For example, the law of gravity does not change over time or space (or even if you believe it may not exist, it will still work!) Your job here is to identify what principles are at play with  • your market • your offering • the opportunity you’re looking to enter into  • the external forces at play (macro economic, social, consumer behavior) Once you’ve identified the principles at play, you use them to anchor your thinking from lens 1 & 2 back to reality and create working hypothesis. Then all that’s left is to make your move, measure, optimize etc. 

Bridging the Gaps in AI Management Systems (AIMS) While implementing AI frameworks like ISO/IEC 42001, many organizations create policies and frameworks but struggle with execution. The real challenge? Turning documents into practice. Here’s a common gap assessment sheet👉 and the actions needed in reality: 🔹 Conduct AI Risk / Context Analysis 👉 Map all AI use cases, assess bias, data privacy, and compliance risks using a simple risk matrix. 🔹 Update Stakeholder Register 👉 Capture who is impacted by AI (IT, Risk, Legal, Customers) and their roles – keep it as a living document. 🔹 Draft & Approve AI Policy 👉 Align with EU AI Act, NIST AI RMF, ISO 42001. Get leadership buy-in and sign-off. 🔹 Develop AI Risk Assessment Framework 👉 Define risk categories (bias, explainability, compliance). Use checklists & scoring scales. Pilot with one AI project first. 🔹 Conduct Training Sessions 👉 Tailor sessions for leaders, developers and employees. Include do’s/don’ts (ex don’t feed client data into ChatGPT). 🔹 Document & Implement AI Lifecycle 👉 Define clear stages: Idea → Data → Training → Testing → Deployment → Monitoring → Retirement. Assign ownership. 🔹 Define & Monitor AI Compliance KPIs 👉 Examples: % of models bias-tested, no. of AI incidents logged. Track through dashboards and report to governance committees. 🔹 Expand Incident Management to Cover AI 👉 Add “AI-related” categories in your system. Create playbooks for scenarios like bias detection, data leaks or hallucinations. #AI #RiskManagement #ISO42001 #Governance #ArtificialIntelligence

A robust risk assessment framework is crucial for strategic procurement. This framework helps you analyze categories across key risk dimensions, enabling proactive mitigation and informed decision-making. Risk Areas (rated >> High, Medium, Low, or N/A): 1.⚖️ Regulatory Risk: Potential legal and compliance issues from supplier activities. Considerations: Supplier access to confidential data, interaction with consumers, regulated activities. 2.📣 Reputational Risk: Potential negative publicity and brand damage due to supplier actions. Considerations: Supplier interaction with consumers, inherent category risks. 3.📊 Market Characteristics: (Influences risk levels) >> Bottleneck: Critical category, low spend, limited suppliers, high switching costs. >> Strategic: Critical category, high spend, limited suppliers, high switching costs. >> Leverage: Less critical category, high spend, sufficient suppliers, low switching costs. >> Transactional: Non-critical category, competitive market, low switching costs. 4.⚙️ Impact on Service Delivery: Potential disruption to consumer services. Considerations: Supplier interaction with consumers, direct provision of products/services. 5.🧑💼 Impact on Employees: Potential negative effects on employee well-being and productivity. Considerations: Supplier provision of products/services impacting employee health, welfare, and productivity. 6.💥 Operational Criticality: Importance of the category to agency operations. Considerations: Impact of failure, recovery steps, impact of supplier closure. 7.💡 Innovation/Value Creation Potential: Opportunity for supplier contribution. Considerations: Product improvement, cost reduction, efficiency gains. 8.🎯 Impact on Agency Objectives: Contribution to strategic goals. Considerations: Innovation contribution, continuous improvement/efficiency contribution. Using the Framework: For each spend category, assess the risk level (High, Medium, Low, N/A) for each of the 8 areas. This allows you to prioritize risk management efforts, inform supplier strategies, identify improvement opportunities, and align procurement with agency objectives. This framework empowers you to move from reactive to proactive risk management, building a more resilient and successful procurement function. ♻️ 𝙁𝙤𝙪𝙣𝙙 𝙩𝙝𝙞𝙨 𝙝𝙚𝙡𝙥𝙛𝙪𝙡? 𝙎𝙝𝙖𝙧𝙚 𝙞𝙩 𝙬𝙞𝙩𝙝 𝙮𝙤𝙪𝙧 𝙣𝙚𝙩𝙬𝙤𝙧𝙠 𝙩𝙤 𝙨𝙥𝙧𝙚𝙖𝙙 𝙩𝙝𝙚 𝙠𝙣𝙤𝙬𝙡𝙚𝙙𝙜𝙚! 𝗱𝗼𝗻'𝘁 𝗳𝗼𝗿𝗴𝗲𝘁 𝘁𝗼 𝗳𝗼𝗹𝗹𝗼𝘄 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗶𝗻𝘀𝗶𝗴𝗵𝘁𝘀 #procurement #riskmanagement #supplychainmanagement #spendanalysis #strategicsourcing #supplierrelationships #compliance

Building a Robust Operational Risk Framework: A Practical Approach Strengthening your operational risk framework starts with the basics: risk appetite, risk policy, key risk indicators, and forward-looking matrices. Are you building on a solid foundation? In this post, I'll share my thoughts on how to develop these critical components in a practical and effective way. 1. Risk Appetite: Defining Your Organization's Risk Tolerance Define your risk appetite: the guiding principle for your risk management efforts. What risks will you take, and what's acceptable? To develop a robust risk appetite statement: - Involve senior management and the board in the discussion - Consider your organization's strategic objectives and risk profile - Define risk appetite in terms of specific metrics or thresholds (e.g., probability of loss or financial impact) 2. Risk Policy: Providing Guidance and Direction Your risk policy should outline the principles and guidelines for managing operational risk. It's crucial to ensure that your risk policy is clear, concise, and accessible to all employees. When developing your risk policy: - Align it with your organization's overall risk management framework - Define roles and responsibilities for risk management - Establish procedures for risk identification, assessment, and mitigation 3. Key Risk Indicators (KRIs): Monitoring Risk Exposure KRIs are essential metrics that help you monitor and manage operational risk. They should be tailored to your organization's specific risk profile and provide early warnings of potential risk exposures. When selecting KRIs: - Identify metrics that are relevant, reliable, and measurable - Ensure KRIs are aligned with your risk appetite and policy - Regularly review and update KRIs to reflect changes in your risk profile 4. Forward-Looking Matrices: Anticipating Emerging Risks Forward-looking matrices help you anticipate and prepare for emerging risks. They provide a structured approach to identifying and assessing potential risks and opportunities. When developing forward-looking matrices: - Consider multiple scenarios and potential outcomes - Engage with stakeholders across the organization to gather insights - Regularly review and update matrices to reflect changes in your risk profile 5. Putting it all Together: A Robust Operational Risk Framework Developing a robust operational risk framework requires a structured approach. By defining your risk appetite, establishing a clear risk policy, selecting relevant KRIs, and anticipating emerging risks through forward-looking matrices, you'll be well on your way to managing operational risk effectively. Remember, risk management is an ongoing process. Regularly review and update these components to ensure your framework remains relevant and effective. Building a robust model risk management framework is crucial in today's complex regulatory environment. Check out this insightful report from PwC India

$1M in fraud protection starts with mapping. That’s the goal. Now break it down: • Map every user touchpoint • Assign risk levels to each interaction • Identify high-risk points before fraud does Here’s how to deconstruct your risk surface area: 1. Map the User Journey: • Outline each touchpoint from signup to checkout. • Identify data points where fraud could slip in. 2. Label Risk Levels: • Assign risk levels to each interaction. • Use past data to gauge potential threats. 3. Build Fraud Detection Points: • Integrate checks and controls along the journey. • Automate alerts for suspicious behaviors. Example framework: 1. Map out every single user interaction. 2. Rate each point by risk potential, high to low. 3. Place tailored fraud checks where they matter most. What does this give you? A roadmap of where fraud might hit, long before it does. No more guesswork, just a clear system.

AI Risk Management Framework from the Cloud Security Alliance. Here are the concepts I found actionable from the paper... 1) Comprehensive MRM Framework: Example: Establish a governance committee that oversees AI development, ensuring compliance with industry standards and regulatory requirements. 2) Model Cards: Example: To enhance transparency, create detailed documentation for each AI model outlining its purpose, design, training data, and performance metrics. 3) Data Sheets: Example: Document the sources, quality, and preprocessing steps of training data used for a model to identify potential biases. 4) Risk Cards: Example: Develop risk cards that identify and mitigate potential issues, such as data bias, in hiring models by implementing fairness constraints and diverse training datasets. 5) Scenario Planning: Example: Conduct scenario planning for an AI-powered chatbot to explore how it might handle offensive language or misinformation and develop mitigation strategies. 6) Continuous Monitoring: Example: Set up automated monitoring for a fraud detection model to track its performance and accuracy over time and identify any drifts or anomalies. 7) Prioritize Mitigation: Example: First, focus on high-impact risks, such as implementing strong encryption and access controls for AI systems handling sensitive financial data. 8) Transparency and Trust: Example: Regularly update stakeholders on AI model performance and risk mitigation efforts through transparent reporting and open communication channels. By implementing these steps, you can harness AI’s full potential while minimizing risks. There is no tool you can buy that will do this for you (yet). It’s good old-fashioned process.  💡🔒  #AI #RiskManagement #AIGovernance #ModelRisk #Innovation #CyberSecurity Cloud Security Alliance Caleb Sima

This is could be the start of a journey to protect your organization from variety of risks (not only cyber/technology risks). The first steps are truly to believe and to comprehend the inter-connectivity between all these risk domains of an organization. I always add the financial domain, the holistic (not only cyber) 3rd party/outsourcing risk, the business and compliance/legal risk. All these risk domains overlap in one or another way. The impact on organization will be different for every firm - why? Different markets and business goals across diverse timelines, the technology stacks differ in age, integration level, the human element, location, regulatory environment and mainly the risk appetite is key driver. What I have not heard often or at all is the thought that each action, activity or lack of has an underlying reason - e.g. strategy/business/financial reasons for an M&A, house developing an own CRM system due to a very complex an unusual CMR procedures, not applying a patch due to an old infrastructure and breaking data flow between apps etc. These reasons have benefits as well, not only risks. Nice! But what next? My recommendation, start with a simple structure, not to boil the ocean: 1. Identify, evaluate (best quantify) your risks of the action - what is the likelihood of happening, how many times a year/10 years this event can occur, what the consequences of this risk may cost you. End to end, across all domains, do not isolate. Please consider also risk remediating actions such as security controls or others, which will help to lower the risk value, don’t forget they may add some costs as well. 2. Evaluate the benefits of the action, activity or lack of it, in the same way. 3. Gain/Loss to the organization - compare the benefit with the risk value, this is your indicator (yes an indicator only) whether the event is a risky or beneficial event. 4. If the event’s risks are higher than the benefits, you need to apply your risk (appetite) threshold, which will help to decide whether to execute the action and accept the risk or not. 5. This type of evaluations can demonstrate how much it can costs to make a pro decision for the specific event. 5. The CFO, the leadership should be consulted whether the organization can and is willing to come up for the risk financially (in case it realizes or for the remediating actions), short or long term. Reputational damage, loss of skilled staff, slow re-hiring process, high staff turn around, changed work ethics, incorrectly placed or to be replaced technology and many other soft values risks result in a financial impact. This is very simplified approach. You can vary in simulations of your business resiliency with verifying whether the organization could ‘cover and survive’ financially one or more related events. There is a lot of room for scenarios. Don’t forget, we work with likelihoods, assumptions and the world, that spins fast and changes every day. As the risks and opportunities.