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Problem-Aware

202 posts on problem-aware.

AI Agent Action Lineage: Reconstructing What an Autonomous Agent Did From the Audit Record

AI agent action lineage is the record series that lets a security team reconstruct what an autonomous agent did across a sequence of LLM calls, tool invocations, and downstream actions. The record has to carry the agent identity, the originating user identity, the prompt and response on every step, the policy state, and the cross-references between steps. This piece walks through the lineage record, where it sits, and what audit obligations it satisfies.

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The AI Agent Post-Authentication Gap: Why Identity at Login Is Not Identity at the Tool Call

Most enterprise agent architectures authenticate the user at the start of the session and then let the agent run with a service identity that carries no user context. The gap between the login identity and the per-tool-call identity is the post-authentication gap. This piece walks through the gap, where it shows up in production, the audit record fields it breaks, and the architectural pattern that closes it.

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Shadow AI vs Sanctioned AI: Why the Line Moves Every Quarter

Shadow AI is unauthorized employee use of AI tools. Sanctioned AI is the set of tools the organization has reviewed and approved. The line between them moves every quarter as vendors add LLM features inside SaaS products that were already on the approved list. This piece walks through the operational distinction, why traditional CASB classification fails to keep up, and what the architecture has to look like for the sanctioned-versus-shadow boundary to mean something at the request layer.

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How to Write an AI Usage Policy That Holds Up Under Audit

An AI usage policy that survives a regulatory review covers data classification, identity binding, sanctioned tools, prompt content rules, audit retention, and incident handling. The pattern that fails most often is a policy written for HR distribution that the security team cannot demonstrate compliance with. This piece walks through the eight sections every policy needs, the enforcement layer the policy depends on, and the audit evidence the policy has to produce.

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AI Usage Policy Examples: Six Working Templates by Industry

Working AI usage policy examples have to match the regulatory regime they live under. The healthcare policy turns on PHI and the BAA. The financial services policy turns on MNPI and DORA. The SaaS policy turns on customer data and the EU AI Act deployer obligations. This piece walks through six industry-calibrated policy examples, the specific clauses that distinguish them, and the enforcement layer all six share.

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Shadow AI Breach Examples: Five Patterns That Keep Repeating

Shadow AI breaches now cost an average of $670,000 more than standard breaches and take 247 days to detect, per the IBM 2026 Cost of Data Breach study of 600 organizations. The breach patterns repeat across industries: source code into consumer ChatGPT, PHI into unauthorized models, MNPI in research workflows, customer PII through embedded SaaS AI, and prompt injection on agentic workflows. This piece walks through five patterns, the architectural common cause, and the enforcement layer that removes the surface.

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Agentic AI Compliance: Where the Existing Frameworks Apply and Where They Fall Short

Agentic AI compliance is the application of EU AI Act, NIST AI RMF, ISO 42001, and sector regulations to autonomous AI systems that take actions on behalf of users. The frameworks were written before agentic systems were widely deployed. The Article 12 logging obligation applies. The NIST identity and authorization framework applies. The audit and disclosure obligations apply. The gap is that none of them name the action-level evidence requirement explicitly. This piece walks through where existing frameworks apply, where they fall short, and what the per-action evidence layer has to produce.

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Agentic AI Risk: Mapping the New Failure Modes to Enterprise Controls

Agentic AI risk is the set of failure modes that emerge when AI systems take autonomous actions. The risk register has to extend beyond the chatbot risks (data leakage, prompt injection) to cover unauthorized action execution, identity escalation through static credentials, action lineage gaps, and downstream system impact. This piece walks through the failure modes, the existing control frameworks that apply, and the architectural primitive that closes the per-action enforcement gap.

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Shadow AI Monitoring Tools: What to Measure and Where to Operate

Shadow AI monitoring tools observe employee AI usage that runs outside the IT-sanctioned stack. The category covers browser extensions that intercept ChatGPT and Claude sessions, CASB integrations that surface AI SaaS use, network telemetry that flags AI endpoints, and identity-aware proxies that route AI traffic through a policy point. Most tooling today produces visibility without enforcement. The architectural distinction that matters for compliance is whether the tool can block, redact, or modify AI traffic at the moment of the request, not just record it after the fact.

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Shadow AI Governance Framework: From Discovery to Enforcement

A shadow AI governance framework defines how an enterprise discovers, classifies, controls, audits, and reports on AI usage that runs outside the IT-sanctioned stack. The five layers map onto the EU AI Act Article 26 deployer obligations, the NIST AI RMF Govern function, and the ISO 42001 AI management system. Most organizations have policy and discovery covered. The control and audit layers are where the framework usually stops short of operational coverage. The piece walks through what each layer has to produce.

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Prevent Data Leaks to ChatGPT: The Inspection Point Your Endpoint Stack Lacks

Cloud Radix found 77% of employees using unauthorized AI tools paste sensitive business data into ChatGPT and similar models. The endpoint, network, and email stacks most enterprises run today were tuned for files and email and miss the JSON request body where the prompt actually lives. I walk through the inspection point that closes the gap, the four operations it performs on every prompt, and the audit record it produces for the compliance regimes the deployment is operating under in 2026.

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Prompt Injection Defense in Depth: The Three Inspection Layers That Compose

Prompt injection defense in depth combines three inspection layers: request-path classification that flags suspicious instructions in the prompt, model-side safety training that resists injection during inference, and response-path inspection that catches successful injections in the model output. No single layer catches every attack. The combination produces stronger coverage than any layer in isolation. I walk through what each layer sees, where each one is blind, and how the audit record reconciles the decisions across layers.

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