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Problem-Aware

202 posts on problem-aware.

Vector Database Security Checklist: What to Verify Before the Index Goes to Production

A vector index inherits the sensitivity of every document that went into it, and most access-control models were designed for rows, not for nearest-neighbour search. This checklist covers tenant isolation, permission-aware retrieval, ingestion validation, embedding inversion, deletion, and retrieval logging, mapped to OWASP LLM08:2025 and to what an auditor asks for.

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Agent-to-Agent Authorization: Deciding What a Delegated Agent May Do Before It Calls the Next Model

When one AI agent hands work to another, or calls a tool or a model on a user behalf, authentication proves which agent is calling and authorization decides whether that agent may take this action with this data right now. Many agent stacks solve the first and skip the second, so a delegated agent inherits the full permissions of whatever credential it holds. This piece walks the authorization decision on agent-to-agent traffic, the delegated-authority and action-lineage requirements behind it, and how to enforce and record it on the HTTP calls the agents actually make.

agentic-aiidentity-and-authorizationai-securityai-agentspolicy-enforcementnist-ai-rmfinline-enforcement
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CISA Advisory Exposes AI Model Distillation Transfer Stations

The September 2026 CISA advisory, published with NSA and FBI, describes transfer stations that resell access to frontier models while obscuring origin and spreading requests across accounts and providers. I explain the distributed traffic mechanism and the enterprise egress evidence it changes, along with the exact boundary of an identity-aware HTTP policy gateway.

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Shadow AI vs Shadow IT: Why the Old Detection Stack Misses the AI Request Layer

Shadow IT is the SaaS subscription the security team did not approve. Shadow AI is the LLM the employee opens in the browser tab next to the approved SaaS. The two look similar to the procurement team. They differ at the detection layer the security team built. This piece walks through the four mechanisms shadow IT detection uses, why each one misses the AI request layer, what shadow AI detection has to read instead, and the inspection topology that closes the gap.

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Agentic AI Framework Security: Comparing How LangGraph, AutoGen, and CrewAI Handle the Model-Call Boundary

LangGraph, AutoGen, and CrewAI orchestrate the same core moves: an agent reasons with a model, calls tools, and acts on results. Their security posture depends on how you scope tools, propagate identity, gate actions, and control egress. This comparison walks each framework's approach and names the one control none of them enforce by default: identity-bound policy on the outbound model call.

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Shadow AI Risks: Quantified Loss Exposure, Regulatory Liability, and the Per-Incident Math

Shadow AI risk lives in three separate ledgers: the per-incident breach cost, the regulatory liability that attaches to the deploying organization regardless of which employee pasted what, and the contractual liability already shifting from AI vendors to enterprises. This piece walks through each ledger with the numbers from IBM, the EU AI Act, Fannie Mae, and Gartner, and shows where the architecture closes the exposure.

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Shadow AI Prevention: Why Blocklists Fail and What an Enforcement Architecture Has To Do

Most shadow AI prevention programs ship a blocklist of AI provider domains and call the work done. The block fires for fifteen of the top tools, employees route around it through personal devices and tethered phones, and the prompt traffic the policy was meant to stop continues. This piece walks through what prevention has to do mechanically to hold up under EU AI Act and HIPAA review, and where the enforcement layer sits.

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Shadow AI Policy Template: What a Defensible Internal Policy Actually Contains

A shadow AI policy is the document a regulator reads first when something goes wrong. Most copy-paste templates fail because they list rules without the enforcement architecture behind them. This piece walks through the seven sections a defensible policy contains, the enforcement architecture each section assumes, and where most published templates fall short of what an EU AI Act reviewer or a HIPAA auditor will actually accept.

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Shadow AI Monitoring: What You Can Actually See and Where the Inspection Layer Has To Sit

Most shadow AI monitoring stops at the DNS layer or the CASB. Both miss the actual data leaving the organization because the prompt is the data, and the prompt sits inside an encrypted POST body. This piece walks through the four monitoring layers, what each one sees, where each one is blind, and the inspection architecture that produces evidence an EU AI Act or HIPAA auditor will accept.

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Employee ChatGPT Monitoring: The Practical Architecture and What It Has To Say in the Handbook

Most employee ChatGPT monitoring conversations get stuck on whether the organization is allowed to do it. The answer in most jurisdictions is yes, provided the disclosure language in the handbook is correct and the inspection is proportionate to the security purpose. This piece walks through the disclosure model that holds up under labor review, the inspection architecture that produces evidence, and what an employee policy actually has to say.

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Shadow AI Discovery Framework: The Six-Week Path From Blind to Inventoried

Most organizations that decide to address shadow AI start by buying a tool. The tool deploys, fires alerts on day one, and produces a report nobody can act on. A working discovery program is a sequenced six-week framework that begins with what the organization already has (DNS logs, expense reports, SSO data) and adds inspection only after the surface is mapped. This piece walks through the framework week by week.

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AI Agent Identity Management: From Issuing an Identity to Recording Action Lineage

Non-human identities now outnumber humans by more than 80 to 1 in the average enterprise, and every one of those tokens can call an LLM. This overview walks through what an AI agent identity is, the four-stage lifecycle from issuance to authorization to recorded action lineage, and where NIST puts the control boundary between the application and the enforcement layer.

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