Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.
SEC AI washing enforcement turns a broad communications problem into a specific evidence test. Investment advisers and public companies should inventory every AI statement, identify the system and data behind it, route claims through legal and technical review, preserve dated substantiation, and monitor production changes that make approved language stale.
Read post →The preliminary NIST Cyber AI Profile applies Cybersecurity Framework 2.0 to three connected jobs: securing AI systems, using AI in cyber defense, and resisting AI-enabled attacks. Security leaders should turn the profile into a current-state record, a target profile, named control owners, and evidence for each claimed outcome while keeping technical overlays and AI risk governance in their proper roles.
Read post →LLM evaluation tools produce different kinds of evidence: deterministic test results, rubric scores, RAG metrics, experiment comparisons, and production-quality signals. The right choice starts with the decision a team needs to make and the artifact required to defend it. This guide compares OpenAI Datasets and Evals, MLflow GenAI, LangSmith, and Ragas by test method, lifecycle stage, data handling, and output, then draws the boundary between evaluation and runtime gateway enforcement.
Read post →Human-in-the-loop agent control requires more than an approval button. A defensible implementation pauses the exact tool call, persists its identity and arguments, records the reviewer decision, and revalidates policy before execution resumes. This article maps that state machine, including nested-agent approvals, stale decisions, duplicate resumes, and the boundary between an HTTP policy decision and the orchestrator that performs the approved action.
DSPM for AI starts with sensitive data: where it lives, who can reach it, how AI apps and agents interact with it, and which exposure should be remediated. Microsoft Purview documentation shows the category spanning continuous data scanning, risk assessment, AI activity, DLP signals, audit, and investigation. This article separates repository and SaaS posture from the inline policy decision on an authenticated HTTP request to an LLM.
Read post →AI sovereignty reaches beyond the region that hosts an LLM. The European Commission framework covers eight objectives across strategy; jurisdiction; data and AI; operations; supply chain; technology; security and compliance; and sustainability. Following one authenticated model request through those layers separates controls that can be enforced at the HTTP boundary from questions that require provider, procurement, operational and infrastructure evidence.
Read post →MCP connects an agent host to tools and resources. A2A connects remote agents and carries delegated work through tasks and messages, with parts and artifacts. The protocols are complementary, and one workflow may use both before either agent calls an LLM. This comparison traces those separate hops, identifies the authorization and audit artifact at each boundary, and marks the narrow HTTP model-request layer DeepInspect can govern.
Claude can support HIPAA-regulated workloads only when an eligible Anthropic commercial service is covered by an accepted Business Associate Agreement, HIPAA mode is activated where required, the chosen capability is included, and the healthcare organization applies its own access and audit controls. This guide follows one PHI-bearing request through those four gates and marks the exact limits of API enforcement.
Read post →ChatGPT can support HIPAA-regulated work only inside an eligible OpenAI product, under a signed Business Associate Agreement, with covered features and the healthcare organization's own safeguards in place. Consumer access and a generic enterprise subscription are insufficient answers for a deployment review. This guide traces a PHI-bearing request through the product, contract, feature, minimum-necessary, and audit checks that determine eligibility.
Read post →AI TRiSM spans governance, model interpretability and explainability, AI data protection, ModelOps, resistance to adversarial attacks, continuous monitoring, validation, runtime enforcement, and evidence. Following one AI request through five distinct control planes shows which evidence exists before, during, and after inference, where an HTTP enforcement gateway fits, and which specific operational responsibilities remain with governance, model, data, deployment, and monitoring owners.
Read post →OpenRouter pricing combines a platform fee with provider inference charges passed through at the selected model and provider price. The live pricing page lists 5.5% for Standard and 8% for Business. BYOK requests are free up to the stated monthly allowance, after which a 5% fee applies. This guide traces each component, explains the separate evidence behind route selection, and identifies the request records needed to account for realized cost.
Read post →The OECD AI Principles were amended in May 2024 to address general-purpose and generative AI, information integrity, foreseeable misuse, privacy and intellectual property, lifecycle safety, system override, and environmental sustainability. This guide maps those additions to the records, evaluations, incident procedures, and request-level evidence an organization needs, while clearly separating HTTP enforcement evidence from fairness, provenance, environmental, and enterprise-accountability work.
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