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Compliance & Regulation

305 posts on compliance & regulation.

ISO 42001 Implementation Guide: How to Stand Up an AI Management System That Passes Certification

ISO/IEC 42001:2023 is the first international management-system standard for AI. The standard takes the ISO management-system structure (the same Annex SL Harmonized Structure used in ISO 9001, ISO 27001, and ISO 14001) and applies it to AI. Certification requires a documented AI management system covering scope, leadership, planning, support, operations, performance evaluation, and improvement. This piece walks through the certification path step by step, the Annex A controls that have to be operational, the audit evidence the certification body expects, the implementation timeline a typical mid-market organization runs, and where the AI-specific controls intersect the inspection-layer architecture.

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ISO 42001 vs ISO 27001: How the AI Management System Layers on Top of Information Security

ISO 42001 and ISO 27001 share the same management-system structure (the Annex SL Harmonized Structure) and a substantial portion of the Annex A control catalog. Organizations with an ISO 27001 certification have a head start on ISO 42001 because the management-system processes transfer with modifications. The two standards address different risk domains: 27001 covers information security risks to confidentiality, integrity, and availability of information assets, while 42001 covers AI-specific risks to fairness, reliability under adversarial pressure, transparency, accountability, and the responsible use of AI systems. This piece walks through the structural overlap, the additive AI-specific controls 42001 introduces, the integration pattern for combined audits, and the inspection-layer architecture that produces evidence under both standards.

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Implementing EU AI Act Article 12 Logging: An Architectural Walkthrough

Article 12 of the EU AI Act takes effect August 2, 2026 for high-risk systems. The text requires automatic event recording over the system lifetime, identification of the natural persons involved, and retention for at least six months. This guide walks through the architecture that satisfies the mandate, the four decisions that have to be made at the request layer, and the audit-record schema that survives a regulator review.

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NIST AI RMF Mapping for AI Gateways: How the Four Functions Land on Request-Layer Controls

The NIST AI Risk Management Framework (AI RMF 1.0, released January 2023) organizes AI risk controls into four functions: Govern, Map, Measure, Manage. The framework is voluntary, but US federal procurement, Fannie Mae LL-2026-04, and the GSA AI Acquisition Resource Guide all reference it directly. This guide walks each of the four functions to the request-layer control on an AI gateway that satisfies it.

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EU AI Act Classifier: A Free Tool to Score Your AI System Against Annex III High-Risk Categories

The EU AI Act assigns AI systems to four risk tiers (prohibited, high-risk, limited-risk, minimal-risk). The classification determines which obligations apply and when they take effect. This page walks through the classifier the DeepInspect team built to score your AI system against the Annex III high-risk categories, the supporting articles, and the inputs the classifier needs to produce a defensible verdict.

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AI Policy Generator: A Free Tool That Produces a Defensible Internal AI Use Policy in 15 Minutes

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. The DeepInspect AI policy generator takes 12 questions about your organization and produces a defensible policy document with the seven sections an EU AI Act reviewer or a HIPAA auditor will recognize. The output is a markdown file your legal team edits and your CISO signs.

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Due Diligence Is Not Due Care: The AI Compliance Gap That Closes at the Request Layer

Due diligence is the procurement check a deployer runs once when selecting an AI vendor. Due care is the ongoing operational obligation that runs every time the AI system produces a decision. Most enterprises confuse the two. The vendor security questionnaire, the SOC 2 report, and the BAA cover the diligence side. The due care side is the per-decision evidence the regulator reads at audit time. This piece walks through the legal distinction, the regulatory regimes that depend on it, and the request-layer architecture that produces due care evidence on demand.

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State of AI Compliance Q2 2026: The Regulations That Took Effect, the Enforcement Actions That Landed, and the Evidence Gaps Auditors Cited

Q2 2026 closed with the EU AI Act high-risk system requirements 60 days from effect, the Fannie Mae and Freddie Mac AI governance frameworks already in force, and the first major enforcement actions under the EU AI Act risk-management obligations on the docket. This quarterly mini-report walks through the regulations that took effect or shifted in Q2 2026, the enforcement and litigation actions that landed, the recurring evidence gaps auditors cited, and the architectural patterns enterprises adopted to close them.

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AI Governance Tools: What the Category Has To Cover and Where Most Products Stop

The AI governance tools category bundles four very different product shapes: model registries, policy authoring platforms, posture and inventory scanners, and runtime enforcement layers. Each shape covers a different obligation under the EU AI Act, NIST AI RMF, ISO 42001, and Fannie Mae LL-2026-04. This piece walks through what each shape does, where each one stops, and the runtime gap most buyers discover after the procurement decision.

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AI Governance Maturity Model: The Five Stages and Where Most Enterprises Actually Sit

AI governance maturity models tend to read as aspirational ladders that everyone climbs eventually. The version that matches what regulators ask for in 2026 has five concrete stages defined by the per-decision evidence the deployer can produce at each level. This piece walks through the five stages, where each stage sits against EU AI Act Article 12 and Fannie Mae LL-2026-04 obligations, and the architectural control that moves an organization to the next stage.

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AI Governance Failure: What the Headline Incidents Have in Common and Where the Architecture Fails

AI governance failures cluster around the same architectural defects in incident after incident: identity unbound at the request layer, audit logs written by the application under audit, shadow AI traffic outside the inspection boundary, and vendor AI usage the deployer never sees. This piece walks through the recurring failure pattern, the recent incident record, and the architectural control that closes each defect before the next breach gets reported.

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AI Governance Challenges: The Seven Failures That Show Up in the First Regulator Review

AI governance challenges show up in a specific order during the first EU AI Act, NIST AI RMF, and Fannie Mae LL-2026-04 review. The seven failure modes cluster around identity binding, per-decision audit, shadow AI exposure, vendor AI usage, policy version drift, model registry gaps, and disclosure obligations. This piece walks each failure mode through the regulatory question that surfaces it and the architectural control that closes it.

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