How to Mitigate Risks in AI System Interactions

🚨 AI Privacy Risks & Mitigations Large Language Models (LLMs), by Isabel Barberá, is the 107-page report about AI & Privacy you were waiting for! [Bookmark & share below]. Topics covered: - Background "This section introduces Large Language Models, how they work, and their common applications. It also discusses performance evaluation measures, helping readers understand the foundational aspects of LLM systems." - Data Flow and Associated Privacy Risks in LLM Systems "Here, we explore how privacy risks emerge across different LLM service models, emphasizing the importance of understanding data flows throughout the AI lifecycle. This section also identifies risks and mitigations and examines roles and responsibilities under the AI Act and the GDPR." - Data Protection and Privacy Risk Assessment: Risk Identification "This section outlines criteria for identifying risks and provides examples of privacy risks specific to LLM systems. Developers and users can use this section as a starting point for identifying risks in their own systems." - Data Protection and Privacy Risk Assessment: Risk Estimation & Evaluation "Guidance on how to analyse, classify and assess privacy risks is provided here, with criteria for evaluating both the probability and severity of risks. This section explains how to derive a final risk evaluation to prioritize mitigation efforts effectively." - Data Protection and Privacy Risk Control "This section details risk treatment strategies, offering practical mitigation measures for common privacy risks in LLM systems. It also discusses residual risk acceptance and the iterative nature of risk management in AI systems." - Residual Risk Evaluation "Evaluating residual risks after mitigation is essential to ensure risks fall within acceptable thresholds and do not require further action. This section outlines how residual risks are evaluated to determine whether additional mitigation is needed or if the model or LLM system is ready for deployment." - Review & Monitor "This section covers the importance of reviewing risk management activities and maintaining a risk register. It also highlights the importance of continuous monitoring to detect emerging risks, assess real-world impact, and refine mitigation strategies." - Examples of LLM Systems’ Risk Assessments "Three detailed use cases are provided to demonstrate the application of the risk management framework in real-world scenarios. These examples illustrate how risks can be identified, assessed, and mitigated across various contexts." - Reference to Tools, Methodologies, Benchmarks, and Guidance "The final section compiles tools, evaluation metrics, benchmarks, methodologies, and standards to support developers and users in managing risks and evaluating the performance of LLM systems." 👉 Download it below. 👉 NEVER MISS my AI governance updates: join my newsletter's 58,500+ subscribers (below). #AI #AIGovernance #Privacy #DataProtection #AIRegulation #EDPB

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 new guide from the OWASP® Foundation Agentic Security Initiative for developers, architects, security professionals, and platform engineers building or securing agentic AI applications, published Feb 17, 2025, provides a threat-model-based reference for understanding emerging agentic AI threats and their mitigations. Link: https://lnkd.in/gFVHb2BF * * * The OWASP Agentic AI Threat Model highlights 15 major threats in AI-driven agents and potential mitigations: 1️⃣ Memory Poisoning – Prevent unauthorized data manipulation via session isolation & anomaly detection. 2️⃣ Tool Misuse – Enforce strict tool access controls & execution monitoring to prevent unauthorized actions. 3️⃣ Privilege Compromise – Use granular permission controls & role validation to prevent privilege escalation. 4️⃣ Resource Overload – Implement rate limiting & adaptive scaling to mitigate system failures. 5️⃣ Cascading Hallucinations – Deploy multi-source validation & output monitoring to reduce misinformation spread. 6️⃣ Intent Breaking & Goal Manipulation – Use goal alignment audits & AI behavioral tracking to prevent agent deviation. 7️⃣ Misaligned & Deceptive Behaviors – Require human confirmation & deception detection for high-risk AI decisions. 8️⃣ Repudiation & Untraceability – Ensure cryptographic logging & real-time monitoring for accountability. 9️⃣ Identity Spoofing & Impersonation – Strengthen identity validation & trust boundaries to prevent fraud. 🔟 Overwhelming Human Oversight – Introduce adaptive AI-human interaction thresholds to prevent decision fatigue. 1️⃣1️⃣ Unexpected Code Execution (RCE) – Sandbox execution & monitor AI-generated scripts for unauthorized actions. 1️⃣2️⃣ Agent Communication Poisoning – Secure agent-to-agent interactions with cryptographic authentication. 1️⃣3️⃣ Rogue Agents in Multi-Agent Systems – Monitor for unauthorized agent activities & enforce policy constraints. 1️⃣4️⃣ Human Attacks on Multi-Agent Systems – Restrict agent delegation & enforce inter-agent authentication. 1️⃣5️⃣ Human Manipulation – Implement response validation & content filtering to detect manipulated AI outputs. * * * The Agentic Threats Taxonomy Navigator then provides a structured approach to identifying and assessing agentic AI security risks by leading though 6 questions: 1️⃣ Autonomy & Reasoning Risks – Does the AI autonomously decide steps to achieve goals? 2️⃣ Memory-Based Threats – Does the AI rely on stored memory for decision-making? 3️⃣ Tool & Execution Threats – Does the AI use tools, system commands, or external integrations? 4️⃣ Authentication & Spoofing Risks – Does AI require authentication for users, tools, or services? 5️⃣ Human-In-The-Loop (HITL) Exploits – Does AI require human engagement for decisions? 6️⃣ Multi-Agent System Risks – Does the AI system rely on multiple interacting agents?

"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

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.

⚠️ Stop these 9 AI threats before it’s too late. Most teams are racing to adopt AI without realizing they’re opening the door to a whole new category of risks. I’ve seen companies get burned by AI hallucinations in customer service. I’ve watched executives fall for deepfake scams. I’ve seen proprietary code accidentally leaked through ChatGPT prompts. Here’s what keeps me up at night: while we’re all excited about AI’s potential, very few organizations have updated their security playbooks to match this new reality. We’re using yesterday’s defenses against tomorrow’s threats. 📌 The 9 AI Security Risks Every Leader Should Know: 1. HALLUCINATIONS Your AI confidently gives wrong answers. Models predict likely words, not facts. They don't say "I don't know." → Fix: Add verification steps. Require citations. Train users not to trust blindly. 2. PILL EXPOSURE Private data (names, emails, IDs) leaks unintentionally from your prompts or responses. → Fix: Mask sensitive data. Audit logs. Use separate environments for testing. 3. DEEPFAKES & SYNTHETIC MEDIA Fake videos/audio impersonating executives. Scams. Misinformation. → Fix: Detection tools. Watermarking. Train employees on verification. 4. PROMPT INJECTION & DATA LEAKS Attackers exploit AI inputs to access data or change commands. → Fix: Sanitize inputs. Limit model access. Monitor unusual queries. 5. SHADOW AI Employees using unauthorized AI tools without IT knowing. → Fix: AI governance policy. Approved tools list. Regular audits. 6. MODEL BIAS AI supports discrimination or unfair decisions trained on biased data. → Fix: Audit training data. Test for bias. Diverse evaluation teams. 7. IP LEAKAGE Internal code or proprietary data leaks via AI systems. → Fix: Don't paste internal data into public AI. Use private deployments. 8. COMPLIANCE & REGULATION Data privacy violations or AI-related legal breaches. → Fix: Know your regulations (GDPR, DPDPA, AI Act). Document decisions. 9. THIRD-PARTY VULNERABILITIES Exposure via vendors, APIs, or model integrations you depend on. → Fix: Vet vendors. Monitor integrations. Have backup providers 📥 Get Free Access to My AI Data Security Guide Here: https://lnkd.in/gtenUagT Save this post. Share it with your team. Because the best defense against AI risks is knowing they exist in the first place. ___________________________________________ 👋 I’m Amit Rawal, an AI practitioner and educator. Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered. ♻️ Repost if you believe AI isn’t about replacing us… It’s about retraining us to think better. Opinions expressed are my own in a personal capacity and do not represent the views, policies, or positions of my employer (currently Google LLC) or its subsidiaries or affiliates.

Pt safety for AI safety... A recent conversation gave me one of those "why aren't we already doing this?" moments. We're spending enormous energy figuring out how to make #AI safe in healthcare. And we should be. The risks are real and likely to get more acute over time as the usage of #AI in Healthcare grows. What I've been wondering about is why we are treating this risk as something unusual...we already have a well-established, well-tested infrastructure for managing risk to patients sitting in every health system in the country. It's our existing #patientsafety experts and systems. When an AI model or algorithm produces an erroneous clinical result, we should treat it with the same rigor and scrutiny we'd apply to any patient-facing technology or process failure. What does that mean? → Report it through your existing event reporting systems → Execute comprehensive root cause and common cause analyses → Discuss findings in M&M conferences and risk management committees → Apply the hierarchy of controls to eliminate or mitigate the risk going forward We don't need to build something new from scratch. We need to redeploy what we've already built — the structures, the processes, the culture of safety — and extend them to cover AI-related risks. The discipline of #PatientSafety has spent decades researching and deploying best practices for how to interrogate system failures without blame and facilitating the redesign of systems and processes to prevent recurrence. That's exactly the muscle we need right now. The tools are already in your organization. Let's use them. My patient safety colleagues...what am I missing? How do we need to adapt our safety infrastructure to meet the AI moment? #HealthcareAI #QualityImprovement #PatientSafety #AI

Is your team still treating AI systems exactly like regular software when it comes to security? 🤔 I've been digging into NIST's draft Cyber AI Profile (IR 8596), which I think is essential reading for any GRC professional. The comment period closed last Friday, and this guidance confirms something many of us have felt for a while: AI challenges some of the core assumptions behind our traditional security frameworks. Unlike typical software which behaves predictably AI models are probabilistic and keep evolving. That means we face a new class of risks that require us to rethink our approach. A few takeaways for those of us in GRC: 💡 1️⃣ Static Checklists Don't Cut It: Because AI behavior is less predictable, relying solely on fixed checklists risks missing important threats. The guidance encourages adopting risk models designed specifically for AI's unique uncertainties. 2️⃣ New Threats Require New Defenses: Attacks like prompt injection, data poisoning, and model extraction aren't simply variations of traditional threats like malware or SQL injection. These AI-specific risks call for tailored mitigation strategies. 3️⃣ Seeing Beyond Vendor Reports: A SOC 2 report isn't enough anymore. To truly understand AI security, you have to trace data lineage, model origins, and base models. That means gaining much deeper insight into the AI supply chain. 4️⃣ Keep an Eye on AI Models Continuously: The draft stresses ongoing monitoring to catch things like model drift, unexpected behavior, and adversarial manipulation as soon as they happen. For those guiding AI risk and compliance programs, this is a strong nudge to update your frameworks. It also reinforces my conviction that the future belongs to practitioners fluent in both AI's technical landscape and sound governance principles. Although the comment period has closed, I encourage you to review the draft. Understanding this guidance now will help you prepare for the compliance landscape that's taking shape. If you're wrestling with how to handle AI's probabilistic risks, I'd be glad to swap notes on what I'm learning. 🤝 Find the draft here --> https://lnkd.in/gzxHSsQb #AIGovernance #GRC #Cybersecurity #AIrisk #NIST #RiskManagement

30 Critical Questions CIOs and CISOs Must Ask About AI Models Focused on Risk Mitigation, Compliance, and Operational Realities   For CIOs and CISOs, AI adoption demands rigorous scrutiny to balance innovation, compliance, and resilience. Below are 30 actionable questions, organized by risk category and anchored in real-world lessons.   1. Governance and Accountability 1.    Who “owns” the AI model’s compliance lifecycle? 2.    Is there a cross-functional AI governance board (legal, security, data science)? 3.    How are model updates tracked and logged for audit purposes? 4.    What is the protocol for retiring outdated or high-risk models to prevent residual exposure?   2. Data Security and Privacy 5.    Is sensitive data (PII, PHI) anonymized before training? 6.    What encryption standards protect data at rest, in transit, and during processing? 7.    Does the model comply with cross-border data laws (e.g., GDPR, China DSL)? 8.    Can we trace the origin, movement, and transformation of all training and operational data (data lineage and provenance)?   3. Model Risks and Transparency 9.    Can decision logic be audited for both local interpretability and global explainability? 10. How is bias tested pre-deployment and monitored in production? 11. Are synthetic or third-party training datasets validated for integrity and representativeness? 12. What controls exist to detect, mitigate, and govern hallucinations or fabricated outputs in generative AI models?   4. Compliance and Regulatory Alignment 13. Does the model adhere to industry-specific regulations (e.g., HIPAA, PCI-DSS, EU AI Act)? 14. Is there a process to adapt to new laws (EU AI Act, US AI EO, OECD principles)? 15. Are consent mechanisms embedded (e.g., opt-outs, DSAR workflows)? 16. How are AI risks mapped into the enterprise risk management (ERM) framework to align with board-level oversight?   5. Third-Party and Supply Chain Risks 17. Do vendors/partners comply with recognized security/AI standards (ISO 27001, SOC 2, NIST AI RMF)? 18. Are open-source libraries and model hubs continuously monitored and patched? 19. What contractual protections exist if a vendor’s model is compromised (e.g., breach notification, liability)? 20. What is our contingency plan if critical vendor APIs (e.g., OpenAI, Anthropic) face outages, pricing shocks, or withdrawal?   6. Operational and Incident Response 21. How is performance monitored in production (e.g., drift, accuracy degradation)? 22. Does our incident response plan include AI-specific threats (poisoning, adversarial attacks, prompt injections)? 23. Are employees trained to recognize AI-specific threats and social engineering attacks? 24. What is the validated process for retraining models while preventing performance regression or bias reintroduction?   Continue in 1st comment.   Image Source: McKinsey   Transform Partner – Your Strategic Champion for Digital Transformation