← All posts

Problem-Aware

88 posts on problem-aware.

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.

shadow-aimonitoringai-securitygovernanceenterprise-aienforcement
Read post →

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.

agentic-aiai-agentsai-riskai-governanceai-securitycompliance
Read post →

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.

agentic-aiai-agentscomplianceeu-ai-actnist-ai-rmfai-governance
Read post →

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.

shadow-aibreachincidentai-securitydata-lossai-governance
Read post →

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.

ai-usage-policypolicy-templatecomplianceai-governanceshadow-aiindustry
Read post →

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.

ai-usage-policyai-governancecomplianceshadow-aipolicy-templateaudit
Read post →

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.

shadow-aiai-governancesanctioned-aicasbenforcementai-security
Read post →

Employee Copilot Usage Policy: What to Cover and How to Enforce It

Microsoft Copilot, GitHub Copilot, and the family of Copilot products sit inside enterprise workflows where employees handle confidential information by default. The policy that governs how employees use Copilot has to cover data classification, prompt content rules, output handling, attribution, and audit. This piece walks through the seven sections every Copilot usage policy needs, the enforcement layer the policy depends on, and the common mistakes that produce policies the security team cannot demonstrate compliance with.

copilotai-usage-policyshadow-aigovernancepolicy-template
Read post →

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.

ai-agent-post-authentication-gapagentic-aiai-agent-securityai-agent-identityaudit-logs
Read post →

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.

ai-agent-action-lineageagentic-aiai-agent-securityaudit-logseu-ai-act
Read post →

Shadow AI Detection: The Three Signals That Actually Identify Unauthorized LLM Use Inside the Enterprise

Shadow AI detection works on three signals: DNS resolution to known LLM endpoints, HTTP request shape against published API contracts, and identity-bound prompt content captured at the HTTP layer. Network DLP and CASB inventories miss the prompt body because it sits inside TLS to a sanctioned destination. This piece walks through each signal, what the detection misses without inline inspection, and the architectural pattern that produces a per-request record auditors can sample.

shadow-aishadow-ai-detectionai-securityinline-enforcementvisibility
Read post →

AI Agent Privilege Abuse: Why Service Credentials Become Effective Superuser Accounts in Multi-Step Agent Workflows

A typical AI agent runs on a single service credential that combines the permissions of every action the agent might need to take. The credential is the union, not the intersection. An agent decomposing a goal can take any action the credential authorizes, including actions the user never intended to delegate. The post-authentication gap is the difference between "the agent is authenticated" and "this specific action against this specific resource is permitted by the user." Closing the gap requires identity propagation from the user through the agent to each tool call.

agentic-aiai-agentsidentityauthorizationai-securityaudit
Read post →