How to Evaluate AI Model Safety

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

WWLD? What Would Lucian Do? A tribute to a transformative leader, Dr. Lucian Leape. In applying #AI to support future pt safety, Dr. Leape would stress: 1-systems thinking 2-psychological safety 3-transparency 4-learning Here are innovative #AI safety initiatives he might champion: 1. “Latent Hazard Map;" generate a heat map of latent safety threats using a multimodal model continuously reviewing data from EHR event logs, devices, work-order tickets.. to highlight medication-error zones or recurrent staffing/equipment–acuity mismatches to mitigate harm. 🟢 Identify system vulnerabilities; turn scattered, unconnected data into actionable system redesign through robust pattern recognition creating intelligent insight. 2. “Psychological-Safety Radar;” Use NLPs/LLMs to filter shift-handoff transcripts, Slack/Teams chats, and incident-report narratives to understand the staffing atmosphere in real time—flagging blame-heavy language or silence zones. Managers and directors would receive coaching nudges (i.e, “invite perspective from quiet members”). 🟢 Embeds Just Culture and safety measures into daily operations, making invisible behavioral risks visible. 3. "Digital-Twin Pre-Shift Simulator;” ML/DL/Gen AI  models build a digital twin of tomorrow’s unit including; census, patients’ acuity, staff roster, and pharmacy/equipment/supply chain signals. Charge RNs run a simulation to preview likely bottlenecks, device shortages, or high-risk transfers. 🟢 Combines systems engineering and safety design,  teams get foresight rather than hindsight. 4. “Room-Sense Safety Sentinel;” Vision models watch for falls, bed-rail gaps, IV-pump occlusion, postures, ungloved line accesses, and even caregiver fatigue signals. 🟢 Embeds error-prevention design into the physical environment. 5. “Just-Culture Navigator for RCA;” A NLP/LLM model ingests event reports, device logs, staffing records, and policy manuals, then guides the RCA team through a Socratic dialogue:  It connects the dots from a library of past RCAs and event reviews to provide a system improvement perspective. 🟢 Codifies a learning, system-focused RCA approach time from weeks to days. 6. “Oculomics-Driven Cognitive Load Meter;” Eye-tracking in smart glasses or workstation webcams, monitors eye movement velocity and pupil dilation during med prep/complex procedures. It identifies individual’s cognitive overload/fatigue and offers micro-interventions: auto-double-check prompt or deferral to another colleague. 🟢 Uses human factors to design systems that respect biological limits and catching slips/lapses. AI can: 1.     Detect hazards earlier and farther “upstream.” 2.     Support error-resistant environments that ease, not burden, clinicians. 3.     Maintain psychological safety by keeping alerts supportive. #UsingWhatWeHaveBetter Michael Posencheg Lalit Bajaj Jeffrey Glasheen, MD Jennifer Wiler MD, MBA, FACEP Read Pierce Dan Hyman,MD Aarti Raghavan Jeffrey Rakover Joseph Kaempf

AI & Practical Steps CISOs Can Take Now! Too much buzz around LLMs can paralyze security leaders. Reality is that, AI isn’t magic! So apply the same foundational security fundamentals. Here’s how to build a real AI security policy: 🔍 Discover AI Usage: Map who’s using AI, where it lives in your org, and intended use cases. 🔐 Govern Your Data: Classify & encrypt sensitive data. Know what data is used in AI tools, and where it goes. 🧠 Educate Users: Train teams on safe AI use. Teach spotting hallucinations and avoiding risky data sharing. 🛡️ Scan Models for Threats: Inspect model files for malware, backdoors, or typosquatting. Treat model files like untrusted code. 📈 Profile Risks (just like Cloud or BYOD): Create an executive-ready risk matrix. Document use cases, threats, business impact, and risk appetite. These steps aren’t flashy but they guard against real risks: data leaks, poisoning, serialization attacks, supply chain threats.

Recent research highlights a major challenge in AI governance: advanced AI models can deliberately obscure their reasoning, evading oversight and hiding misaligned behavior. 1) Anthropic published research exploring how to audit AI language models for hidden goals or deceptive intentions. See paper below; link: https://lnkd.in/gH_Jpxba Researchers intentionally trained an AI model to take advantage of known mistakes ("biases") in the reward system, causing it to secretly act in ways it knew would earn rewards, even if those actions were inappropriate. They called this hidden motivation "RM-sycophancy." In a test, one team secretly trained a deceptive model while four others attempted audits. Three succeeded by inspecting training data, underscoring the importance of thorough data analysis for AI audits. The researchers hope auditing methods like theirs become standard practice for testing AI alignment. See blog post: https://lnkd.in/gX6JqBH9 2) OpenAI’s research on frontier reasoning models has identified models intentionally exploiting loopholes or "reward hacking" by misleading evaluators or circumventing intended behaviors. It found that while “chain-of-thought” (CoT) monitoring effectively detects misbehavior (as models explicitly reveal malicious intent), penalizing models for explicitly stating these “bad thoughts” caused them to conceal their intentions rather than stopping misbehavior entirely. Thus, OpenAI recommends minimal restrictions on CoT to maintain effective oversight. See: https://lnkd.in/g6cHpj2k 3) Another recent research highlights that models often provide unfaithful CoT reasoning: the explanations given don't always reflect their actual decision-making processes. See: https://lnkd.in/gRKFgRsp Specifically, AI models frequently rationalize biases after the fact ("implicit post-hoc rationalization"), adjust reasoning errors silently ("silent corrections"), or take shortcuts through illogical reasoning. This undermines AI safety approaches relying on monitoring CoT to detect harmful behavior. * * * In a LinkedIn article from this week, Katalina Hernandez "Transparency & Regulating AI When It Can Deceive: The Case for Interpretability" summarizes these findings, emphasizing their regulatory implications, especially for the EU AI Act, which depends largely on transparency, documentation, and self-reporting. Hernandez argues that transparency alone is inadequate because AI systems may produce deceptive yet plausible justifications. Instead, robust interpretability methods and real-time monitoring are essential to avoid superficial compliance and ensure true AI alignment. See: https://lnkd.in/g3QvccPR

ISO 5338 has key AI risk management considerations useful to security and compliance leaders. It's a non-certifiable standard laying out best practices for the AI system lifecycle. And it’s related to ISO 42001 because control A6 from Annex A specifically mentions ISO 5338. Here are some key things to think about at every stage: INCEPTION -> Why do I need a non-deterministic system? -> What types of data will the system ingest? -> What types of outputs will it create? -> What is the sensitivity of this info? -> Any regulatory requirements? -> Any contractual ones? -> Is this cost-effective? DESIGN AND DEVELOPMENT -> What type of model? Linear regressor? Neural net? -> Does it need to talk to other systems (an agent)? -> What are the consequences of bad outputs? -> What is the source of the training data? -> How / where will data be retained? -> Will there be continuous training? -> Do we need to moderate outputs? -> Is system browsing the internet? VERIFICATION AND VALIDATION -> Confirm system meets business requirements. -> Consider external review (per NIST AI RMF). -> Do red-teaming and penetration testing. -> Do unit, integration, and UA testing DEPLOYMENT -> Would deploying  system be within our risk appetite? -> If not, who is signing off? What is the justification? -> Train users and impacted parties. -> Update shared security model. -> Publish documentation. -> Add to asset inventory. OPERATION AND MONITORING -> Do we have a vulnerability disclosure program? -> Do we have a whistleblower portal? -> How are we tracking performance? -> Model drift? CONTINUOUS VALIDATION -> Is the system still meeting our business requirements? -> If there is an incident or vulnerability, what do we do? -> What are our legal disclosure requirements? -> Should we disclose even more? -> Do regular audits. RE-EVALUATION -> Has the system exceeded our risk appetite? -> If an incident, do a root cause analysis. -> Do we need to change policies? -> Revamp procedures? RETIREMENT -> Is there business need to retain model or data? Legal? -> Delete everything we don’t need, including backups. -> Audit the deletion. Are you using ISO 5338 for AI risk management?

Happy Friday! This week in #learnwithmz, let’s talk about 𝐀𝐈 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧𝐬 and why PMs need to lean in. As AI features become core to product roadmaps, evaluating AI systems is no longer just a research problem. It's a product responsibility. Whether you're building copilots, agents, search, or agentic systems, you need to know how to measure what “good” looks like. 𝐓𝐨𝐨𝐥𝐬 & 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐟𝐨𝐫 𝐀𝐈 𝐄𝐯𝐚𝐥𝐬 Ragas: End-to-end evals for RAG pipelines 🔗 https://lnkd.in/g-upbP3p Gaia Eval Harness (Anthropic): Tests groundedness and reasoning in Claude-like models 🔗 https://lnkd.in/ggcasAdQ OpenAI Evals: Structured prompt test harness for model behaviors 🔗 https://lnkd.in/gXNcwvSU Arize AI Phoenix: Evaluation + observability for LLMs in production 🔗 https://lnkd.in/gAb9aguA Giskard: Automated testing for ML model quality and ethics 🔗 https://lnkd.in/gzQ_heQW Bonus read: Aakash Gupta’s breakdown on AI evals is an excellent read https://lnkd.in/gJkCDxFT I have posted before on key evaluation metrics: https://lnkd.in/gx5CBNsG 𝐊𝐞𝐲 𝐀𝐫𝐞𝐚𝐬 𝐭𝐨 𝐖𝐚𝐭𝐜𝐡 (𝐚𝐬 𝐚 𝐏𝐌) Guardrails aren’t optional, they’re product requirements - Groundedness: Is the model hallucinating or based in fact? - Helpfulness: Does it solve the actual user need? - Bias & Harm: How inclusive, fair, and safe are the outputs? - Consistency: Is the model deterministic where it needs to be? - Evaluation Triggers: Can we detect failure modes early? 𝐄𝐱𝐚𝐦𝐩𝐥𝐞 Evaluating an NL2SQL Copilot Goal: User types a question like “Show me the top 5 customers by revenue last quarter” The system should generate correct, optimized SQL against a given schema. 𝐊𝐞𝐲 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐢𝐨𝐧 𝐃𝐢𝐦𝐞𝐧𝐬𝐢𝐨𝐧𝐬 - Correctness (Semantic Accuracy) Does the SQL produce the expected result? Is it aligned with schema constraints (e.g., table and column names)? Automate this with unit tests or snapshot comparisons - Executability Does the generated SQL run without error? You can use test DBs or mock query runners - Faithfulness (Groundedness) Does the SQL only use tables and columns present in the schema? Hallucinated column/table = major fail - Performance/Affordability Is the SQL optimized for cost and latency (no SELECT *)? Use static query analysis or query plan inspection - Helpfulness (UX/Intent Match) Does the SQL actually answer the user's intent? This can require human-in-the-loop eval 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 You can’t ship AI responsibly without evals and you can’t evaluate well without cross-functional design. PMs, DS, and Eng need shared language, goals, and metrics. Which eval tools are in your stack or on your radar? Let’s crowdsource some best practices #AI #ProductManagement #LLM #AIEvals #ResponsibleAI #RAG #AIObservability #LearnWithMZ

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

⛳ Deploying AI systems is fundamentally different (and much harder, IMO) than software pipelines for one key reason: AI models are non-deterministic. While this might seem obvious and unavoidable, shifting our mindset toward reducing it can make a significant impact.  The closer you can get your AI system to behave like a software pipeline, the more predictable and reliable it’ll be. And the way to achieve this is through solid monitoring and evaluation practices in your pipeline—a.k.a, observability. Here are a just a few practical steps: ⛳ Build test cases: Simple unit tests and regression cases to systematically evaluate model performance. ⛳ Track interactions: Monitor how models interact with their environment, including agent calls to LLMs, tools, and memory systems. ⛳ Use robust evaluation metrics: Regularly assess hallucinations, retrieval quality, context relevance, and other outputs. ⛳ Adopt LLM judges for complex workflows: For advanced use cases, LLM judges can provide nuanced evaluations of responses. A great tool for this Opik is by Comet, an open-source platform built to improve observability and reduce unpredictability in AI systems. It offers abstractions to implement all these practices and more. Check it out: https://lnkd.in/gAFmjkK3 Tools like this can take you a long way in understanding your applications better and reducing non-determinism. I’m partnering with Comet to bring you this information.

What is the importance of "Test, Evaluation, Verification, and Validation" (TEVV) throughout the AI Lifecycle? TEVV tasks are performed throughout the AI lifecycle. (I) Aligning TEVV parameters to AI product requirements can enhance contextual awareness in the AI lifecycle (ii) AI actors who carry out Verification and Validation tasks are distinct from those who perform Test and evaluation actions (iii) TEVV tasks for design, planning, and data may center on internal and external validation of assumptions for system design, data collection, and measurements relative to the intended context of deployment or application. (iv)TEVV tasks for development (i.e., model building) include model validation and assessment. (v)TEVV tasks for deployment include system validation and integration in production, with testing, and recalibration for systems and process integration, user experience, and compliance with existing legal, regulatory, and ethical specifications. (vi) TEVV tasks for operations involve ongoing monitoring for periodic updates, testing, and subject matter expert (SME) recalibration of models, the tracking of incidents or errors reported and their management, the detection of emergent properties and related impacts, and processes for redress and response. Source: NIST AI RMF Figure: NIST AI RMF - Lifecycle and Key Dimensions of an AI System. #ai #artificialintelligence #llm #risklandscape #security #test #evaluation #verification #validation #ailifecycle #nist

Treasure trove from MIT AI Risk Repository - highlights: 👉 1600 risks extracted from 43 taxonomies 👉 Two structured frameworks: Causal Taxonomy: Who, why, and when risks occur. Domain Taxonomy: The "what" across 7 risk domains and 23 subdomains. 👉 Every risk classified by: Entity: Human, AI, or ambiguous. Intent: Intentional or accidental. Timing: Pre-deployment, post-deployment, or unspecified. 👉 Risk Areas: Discrimination & Toxicity Privacy & Security Misinformation Malicious Actors & Misuse Human-Computer Interaction Socioeconomic & Environmental Harms AI System Safety, Failures & Limitations My go-to-resource to assess AI Risk Management. #aigovernance #airisk