AI-Driven Risk Management Strategies

One of the most important contributions of Google DeepMind's new AGI Safety and Security paper is a clean, actionable framing of risk types. Instead of lumping all AI risks into one “doomer” narrative, they break it down into 4 clear categories- with very different implications for mitigation: 1. Misuse → The user is the adversary This isn’t the model behaving badly on its own. It’s humans intentionally instructing it to cause harm- think jailbreak prompts, bioengineering recipes, or social engineering scripts. If we don’t build strong guardrails around access, it doesn’t matter how aligned your model is. Safety = security + control 2. Misalignment → The AI is the adversary The model understands the developer’s intent- but still chooses a path that’s misaligned. It optimizes the reward signal, not the goal behind it. This is the classic “paperclip maximizer” problem, but much more subtle in practice. Alignment isn’t a static checkbox. We need continuous oversight, better interpretability, and ways to build confidence that a system is truly doing what we intend- even as it grows more capable. 3. Mistakes → The world is the adversary Sometimes the AI just… gets it wrong. Not because it’s malicious, but because it lacks the context, or generalizes poorly. This is where brittleness shows up- especially in real-world domains like healthcare, education, or policy. Don’t just test your model- stress test it. Mistakes come from gaps in our data, assumptions, and feedback loops. It's important to build with humility and audit aggressively. 4. Structural Risks → The system is the adversary These are emergent harms- misinformation ecosystems, feedback loops, market failures- that don’t come from one bad actor or one bad model, but from the way everything interacts. These are the hardest problems- and the most underfunded. We need researchers, policymakers, and industry working together to design incentive-aligned ecosystems for AI. The brilliance of this framework: It gives us language to ask better questions. Not just “is this AI safe?” But: - Safe from whom? - In what context? - Over what time horizon? We don’t need to agree on timelines for AGI to agree that risk literacy like this is step one. I’ll be sharing more breakdowns from the paper soon- this is one of the most pragmatic blueprints I’ve seen so far. 🔗Link to the paper in comments. -------- If you found this insightful, do share it with your network ♻️ Follow me (Aishwarya Srinivasan) for more AI news, insights, and educational content to keep you informed in this hyperfast AI landscape 💙

"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.

AI PR Nightmares Part 2: When AI Clones Voices, Faces, and Authority. What Happened: Last week, a sophisticated AI-driven impersonation targeted White House Chief of Staff Susie Wiles. An unknown actor, using advanced AI-generated voice cloning, began contacting high-profile Republicans and business leaders, posing as Wiles. The impersonator requested sensitive information, including lists of potential presidential pardon candidates and even cash transfers. The messages were convincing enough that some recipients engaged before realizing the deception. Wiles’ personal cellphone contacts were reportedly compromised, giving the impersonator access to a network of influential individuals. This incident underscores a huge growing threat: AI-generated deepfakes are becoming increasingly realistic and accessible, enabling malicious actors to impersonate individuals with frightening accuracy. From cloned voices to authentic looking fabricated videos, the potential for misuse spans politics, finance, and way beyond. And it needs your attention now. 🔍 The Implications for PR and Issues Management: As AI-generated impersonations become more prevalent, organizations must proactively address the associated risks as part of their ongoing crisis planning. Here are key considerations: 1. Implement New Verification Protocols: Establish multi-factor authentication for communications, especially those involving sensitive requests. Encourage stakeholders to verify unusual requests through secondary channels. 2. Educate Constituents: Conduct training sessions to raise awareness about deepfake technologies and the signs of AI-generated impersonations. An informed network is a critical defense. 3. Develop a Deepfakes Crisis Plan: Prepare for potential deepfake incidents with a clear action plan, including communication strategies to address stakeholders and the public promptly. 4. Monitor Digital Channels: Utilize your monitoring tools to detect unauthorized use of your organization’s or executives’ likenesses online. Early detection and action can mitigate damage. 5. Collaborate with Authorities: In the event of an impersonation, work closely with law enforcement and cybersecurity experts to investigate and respond effectively. ———————————————————— The rise of AI-driven impersonations is not a distant threat, it’s a current reality and only going to get worse as the tech becomes more sophisticated. If you want to think and talk more about how to prepare for this and other AI related PR and issues management topics, follow along here with my series or DM if I can help your organization prepare or respond.

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

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.

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

Mastering the Architecture of Risk: A Quant’s Blueprint for Modern Financial Stability The Risk Management Framework: A Closer Look A firm’s risk management structure consists of five key areas, each integrating quant models for predictive insights: → Operational Risk: Focuses on internal processes, with roles like Capital & Risk Managers, Data & Metrics, and Modeling. → Credit Risk: Handles default risk and counterparty exposure, utilizing ML models for predictive analytics. → Market Risk: Uses VaR, stochastic volatility, and PCA for factor analysis and hedging market movements. → Liquidity & Treasury Risk: Ensures liquidity with Cashflow-at-Risk models and real-time funding strategies. → Infrastructure & Analytics: Supports quant-driven decision-making through model validation, data pipelines, and AI-driven insights. How Quants Drive Risk Management Quants are at the core of modern risk management, using stochastic models, AI, and reinforcement learning to optimize decisions. → Market Risk: ✔ BlackRock’s reinforcement learning models simulated tail events 10x faster, reducing portfolio drawdowns by 14% during the 2025 Liquidity Squeeze. → Credit Risk: ✔ Morgan Stanley’s ML-driven Probability of Default (PD) model flagged high-risk sectors six months early, saving $1.2B in corporate loan losses. → Liquidity Risk: ✔ Goldman Sachs’ Liquidity Buffers 2.0 dynamically adjusted reserves in real-time, cutting funding gaps by 22% in the 2024 repo crisis. These advances show how quants translate data into actionable risk insights, meeting Basel IV’s new explainable AI mandates. Emerging Trends: Where Risk Meets AI & Quantum As financial complexity increases, firms are integrating AI, reinforcement learning, and quantum optimization into risk models: → AI & Generative Modeling: ✔ Bloomberg’s “SynthRisk” generates 10M+ synthetic crisis scenarios to train resilient risk models. ✔ Citadel’s RL-driven treasury system autonomously hedges FX exposure, saving $220M annually in slippage. → Regulatory Arbitrage & Basel IV: ✔ EU banks use quantum annealing to optimize Risk-Weighted Assets (RWA), freeing up $15B in trapped capital. → Ethical AI & Bias-Free Risk Models: ✔ The 2026 SEC mandate requires federated learning to prevent bias in credit scoring and risk assessments. The Bottom Line Risk management is no longer just about avoiding disasters—it’s about engineering resilience while optimizing for alpha. For quants, this means: → Translating Basel IV constraints into convex optimization problems. → Turning unstructured data (news, tweets, satellite imagery) into real-time risk signals. → Balancing AI’s predictive power with explainability for compliance and interpretability. How are you reinventing risk frameworks in the AI era? Let’s discuss. #RiskManagement #QuantFinance #FinancialEngineering #MarketRisk #AIinFinance #BaselIV #LiquidityRisk #HedgeFunds #TradingStrategies #MachineLearning #AlgorithmicTrading

Harsh truth: AI has opened up a Pandora's box of threats. The most concerning one? The ease with which AI can be used to create and spread misinformation. Deepfakes (AI-generated content that portrays something false as reality) are becoming increasingly sophisticated & challenging to detect. Take the attached video - a fake video of Morgan Freeman, which looks all too real. AI poses a huge risk to brands & individuals, as malicious actors could use deepfakes to: • Create false narratives about a company or its products • Impersonate executives or employees to damage credibility • Manipulate public perception through fake social media posts The implications for PR professionals are enormous. How can we maintain trust and credibility in a world where seeing is no longer believing? The answer lies in proactive preparation and swift response. Here are some key strategies for navigating the AI misinformation minefield: 🔹 1. Educate your team: Ensure everyone understands the threat of deepfakes and how to spot potential fakes. Regular training is essential. 🔹 2. Monitor vigilantly: Keep a close eye on your brand's online presence. Use AI-powered tools to detect anomalies and potential threats. 🔹 3. Have a crisis plan: Develop a clear protocol for responding to AI-generated misinformation. Speed is critical to contain the spread. 🔹 4. Emphasize transparency: Build trust with your audience by being open and honest. Admit mistakes and correct misinformation promptly. 🔹 5. Invest in verification: Partner with experts who can help authenticate content and separate fact from fiction. By staying informed, prepared, and proactive, PR professionals can navigate this new landscape and protect their brands' reputations. The key is to embrace AI as a tool while remaining vigilant against its potential misuse. With the right strategies in place, we can harness the power of AI to build stronger, more resilient brands in the face of the misinformation minefield.

My 5 predictions on how risk & compliance strategies will change in 2025...💭 From AI breakthroughs to global compliance challenges, here’s what I think will shape risk and compliance strategies in financial services in 2025: 🚀 Generative AI becomes the everyday Copilot of risk experts: GenAI will go beyond being an assistant in pure code generation and become the trusted Copilot for risk and compliance teams. By 2025, it’ll play a central role in assisting teams to create, refine, and test risk models, helping teams work faster and more precisely with complex decision logic. The winners? Those who combine AI tools with robust testing frameworks to iterate confidently in high-stakes environments. 📄 AI and data regulation redefines compliance strategies: As AI and data regulations become more prescriptive, fintechs will prioritize governance frameworks that ensure compliance while fostering innovation. For instance, explainability requirements in AI-driven decision systems will reshape how models are built and audited. Teams that integrate transparency and compliance into their workflows—without slowing down—will gain a real edge. 🔃 Real-time, adaptive risk and fraud modeling becomes a must-have: Static models updated once a quarter won’t cut it anymore. With fraud tactics evolving rapidly and market conditions shifting constantly, adaptive, real-time models will be essential. Fintechs will need tools that let them adjust risk and fraud logic on the fly. The frontrunners will be those who can integrate cutting-edge fraud detection providers the fastest. 🌐 Data sovereignty demands more flexible, localized compliance: As cross-border expansion becomes more prominent among leading fintech companies, managing data across diverse regulatory environments will become increasingly complex. Meeting localized compliance will be critical, whether it’s tailoring underwriting to country-specific rules or meeting regional KYC/KYB standards. Teams that can quickly navigate the complexity of integrating local data sources while maintaining oversight over their global product strategies will be the best positioned to scale. 🏦 Large institutions will race for open banking compliance readiness: Although the CFPB’s open banking rules under Section 1033 won’t take effect until 2026, 2025 will see major financial institutions investing heavily in data-sharing infrastructures. These efforts will ensure compliance with the new requirements while positioning themselves to compete in the evolving open banking landscape. For many, this will mean overhauling internal systems, strengthening partnerships with fintechs, and proactively aligning their strategies to leverage expanded data-sharing capabilities. Early movers will lay the groundwork for the next wave of open banking in the US. What are your predictions for next year? I’d love to hear your thoughts!