← All posts

Problem-Aware

192 posts on problem-aware.

The Accountability Gap in Agentic AI Pipelines: Who Owns the Decision When the Agent Acts

Agentic AI pipelines compose multiple model calls, tool invocations, and external retrievals into a single autonomous workflow. The compositional structure produces an accountability gap: the record series the application keeps shows the workflow outcome, the record series the model provider keeps shows the inference call, and neither shows who authorized the agent to act with whose authority at the moment of action. This piece walks through where the gap appears in production pipelines, the structural reason application logs cannot close it, and the inspection-layer record series that produces a defensible answer.

agentic-aiai-agent-accountabilityaudit-logsinline-enforcementnist-ai-rmfai-security
Read post →

AI Security for Internal Copilots: The Identity, Data, and Audit Controls a Production Deployment Has To Run

Internal copilots reach across the organization with the user identity that opens the copilot session and the application identity that calls the model. The boundary between the two identities is where the security and audit obligations sit. This piece walks through the request-time data the copilot reads, the identity-aware policy decisions the deployment has to commit, the audit record format that survives EU AI Act and HIPAA review, and the architectural pattern that closes the post-authentication gap most internal copilots leave open.

internal-copilotai-securityidentity-awareaudit-logsinline-enforcementpost-authentication-gap
Read post →

AI Security for Customer Support Bots: The Inspection Layer Between the Bot and the Customer Data

Customer support bots reach customer records, payment data, account history, and PII at request time through tool calls and retrieval. The bot reads customer data into the LLM context window, the LLM reasons over it, and the bot acts on the result. The security boundary sits at the HTTP path between the bot and the upstream LLM and tool APIs. This piece walks through the data flows the bot exercises in production, the identity and authorization decisions the inspection layer commits, the audit record the customer auditor and the regulator consume, and the prompt-injection surface that retrieved customer content opens.

customer-support-botai-securityidentity-awareaudit-logsindirect-prompt-injectionpii
Read post →

AI Security for RAG Systems: The Inspection Layer Between the Retrieval Output and the Model Call

Retrieval-augmented generation systems read documents from a vector store or a search backend into the model context window before the model reasons. The retrieval step is the point where the system pulls content of varying provenance, authorization, and trustworthiness into the prompt. The security boundary sits at the HTTP path between the retrieval output and the model call. This piece walks through the threat model RAG opens, the identity and authorization decisions the inspection layer commits, the audit record for retrieval-derived content, and the indirect prompt injection surface the retrieved documents expose.

ragai-securityindirect-prompt-injectionidentity-awareaudit-logsretrieval
Read post →

Shadow AI Discovery Quiz: A 12-Question Tool to Score Your Organization Against the Six-Week Discovery Framework

Most organizations that decide to address shadow AI start by buying a tool. The tool fires alerts on day one and produces a report nobody can act on. A working discovery program is a sequenced six-week path that begins with what the organization already has and adds inspection only after the surface is mapped. This 12-question quiz scores your organization against each step of the framework and tells you where the next two weeks of work belongs.

shadow-aidiscoveryai-inventoryself-assessmentfree-toolai-governance
Read post →

Audit Log Validator: A Free Tool That Checks Your AI Audit Records Against EU AI Act and NIST Field Requirements

AI audit records that look complete in a Kibana dashboard often fail an Article 19 field check. The validator takes a sample of your AI audit records and reports which fields are present, which are absent, and which are present in a form that will not survive a regulator's read. The check runs against EU AI Act Article 19, NIST AI RMF MANAGE 1.3, and Fannie Mae LL-2026-04 evidence requirements.

audit-logseu-ai-actnist-ai-rmflog-validatorcompliancefree-tool
Read post →

Securing CI Pipelines from AI Agent Supply-Chain Attacks

CI pipelines now run coding agents on every pull request. The agent reads the repo, pulls down third-party packages, asks an LLM to write code, executes the suggestion, and pushes a commit. Each step is an attack surface a 2024-era CI threat model did not contemplate. This piece walks through the supply-chain attacks that already shipped in production CI in 2026, where the control point sits at the AI request boundary, and the per-decision audit record a forensic investigator needs to reconstruct the incident.

ai-agent-supply-chainci-cdai-securityinline-enforcementagentic-aiaudit-logs
Read post →

AI Agent Supply Chain Attacks: How the Request Boundary Becomes the Failing Surface

AI agent supply chain attacks compromise the agent at one of three points: the model artifact, the tool the agent calls, or the runtime input the agent processes. The HTTP request boundary between the authenticated agent and the LLM endpoint sits underneath all three failure modes. This piece walks through the attack patterns reported in 2025 and 2026, the architectural defects that enable each one, and the inspection-layer control that closes the runtime side of the supply chain risk.

ai-agent-securitysupply-chainagentic-aiprompt-injectioninline-enforcementai-security
Read post →

ChatGPT Prompt Injection: How the Attack Surfaces in Enterprise ChatGPT Deployments

ChatGPT prompt injection attacks reach enterprise deployments through three vectors: the Custom GPT instruction-leak surface, the file-upload indirect injection path, and the connected-tool authorization gap that ChatGPT Enterprise opens through GPT actions. This piece walks through each vector, the failure mode the model alone cannot close, and the request-boundary control that produces a deterministic decision and an audit record EU AI Act Article 12 reviewers will accept.

prompt-injectionllm-securityai-securityshadow-aiinline-enforcementaudit
Read post →

How to Prevent Prompt Injection: The Four Control Layers That Hold Up in Production

Prompt injection prevention splits into four control layers: prompt construction discipline, retrieval-time content evaluation, request-boundary policy enforcement, and post-response output checks. The first two are application work. The third sits in the inspection layer at the HTTP path between the application and the model. This piece walks through what each layer can and cannot prevent, and the architectural pattern that produces a defensible posture under EU AI Act Article 12 and OWASP LLM01 review.

prompt-injectionllm-securityai-securityinline-enforcementpolicy-enforcementowasp
Read post →

Gemini Prompt Injection: The Workspace Integration Surface and the Inspection Layer Response

Gemini prompt injection reaches enterprise deployments through three surfaces that the consumer discussion rarely covers: the Workspace integration path where Gemini reads Gmail, Drive, and Calendar content into the model context, the Gemini API file and URL inputs, and the Vertex AI authorization gap when Gemini is wired into enterprise tools. This piece walks through each surface, the model defenses that fall short, and the request-boundary controls that produce a defensible audit record.

prompt-injectionllm-securityai-securityinline-enforcementauditshadow-ai
Read post →

Claude Prompt Injection: Where the Constitutional AI Defense Falls Short of Enterprise Policy

Claude prompt injection attacks reach enterprise deployments through Anthropic Computer Use, the Files API indirect injection surface, and the MCP connector authorization gap that the Claude developer platform opens. Constitutional AI reduces compliance with the simpler payloads. The training does not enforce the enterprise policy, the user role, or the data classification rules that apply inside a specific organization. This piece walks through each surface and the inspection-layer controls that produce a defensible posture.

prompt-injectionllm-securityai-securityagentic-aiinline-enforcementaudit
Read post →