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Analysis on enterprise AI governance, inline policy enforcement, agentic AI security, and regulatory compliance.

When the LLM Is the Attacker''s Hands: CVE-2026-39987 and the Case for Per-Decision Audit Logging

On May 10, 2026, The Hacker News documented an incident where attackers exploited CVE-2026-39987 in Marimo (≤0.20.4) to gain pre-auth RCE inside a victim AWS environment, harvested credentials, and then drove an LLM agent to operate AWS Secrets Manager on their behalf. The LLM was the post-exploitation tool. This article walks the attack path and explains why the per-decision audit log of LLM traffic just acquired forensic and regulatory weight that legacy CloudTrail data lacks.

Problem-Awareai-securityllm-securityagentic-aiforensic-auditauditcybersecurity
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EU AI Act Deployer Checklist: 22 Items Every Enterprise Deployer Needs Before August 2, 2026

August 2, 2026 is the enforcement date for the high-risk system obligations under Chapter III, Section 2 of the EU AI Act. Most enterprise compliance teams have a checklist for the provider-side obligations. Fewer have a structured checklist for the deployer side, where the runtime-evidence obligation lands. This article walks through 22 specific items a deployer of a high-risk AI system needs to have in place before August 2, organized into pre-deployment artifacts, runtime-evidence infrastructure, human oversight workflow, notification mechanisms, and ongoing operational requirements. Each item references the specific article of the act it satisfies.

Compliance & Regulationeu-ai-actdeployer-obligationscompliance-checklistarticle-26enforcementaugust-2026
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AI Red Team Methodology: A Six-Phase Framework for Adversarial Testing of LLM Applications

Most AI red team engagements run as ad-hoc prompt-injection tests against a chat interface and call the result a red team. A defensible methodology runs through six phases: scope and threat modeling, identity-context attacks, content-vector attacks, agent-layer escalation, multi-turn and persistence attacks, and post-engagement reporting against a remediation owner. This article walks through each phase, the techniques each phase deploys, the evidence the red team should capture, the remediation owner each finding routes to, and the integration points with the rest of the security program.

Platform & Architectureai-red-teamadversarial-testingprompt-injectionagent-securitysecurity-testing
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The AI Vendor Security Questionnaire: 38 Questions Procurement Should Actually Ask

Most AI vendor security questionnaires are SOC 2 templates with two AI questions tacked on. The result is a procurement process that surfaces well-formatted SOC 2 reports while leaving the AI-layer risks unmapped. This article walks through 38 questions that surface what the vendor actually does at the AI request boundary: model coverage, identity context, per-decision audit, policy enforcement, prompt-injection handling, data residency, regulatory alignment, and incident response. The questions assume the vendor is supplying an AI-using service, not a model. Each question includes the answer pattern a defensible vendor produces and the answer pattern that should trigger a deeper review.

Comparisons & Alternativesvendor-managementprocurementai-governancesecurity-questionnairethird-party-risk
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AI Incident Response Playbook: Detection, Containment, and Forensics for AI-Layer Compromises

Most enterprise incident response playbooks assume the compromise sits at the network, endpoint, or application layer. AI-layer incidents (prompt injection in production, agent tool-call escalation, model-extraction attempts, credential theft via LLM-operated post-exploitation, data exfiltration through prompts) require a different detection signal, a different containment action, and a different forensic timeline. This playbook walks through the AI-layer incident classes the SOC should recognize, the detection signals each class produces, the containment actions that work at the AI request boundary, the forensic evidence the post-mortem needs, and the integration points with the rest of the security operations stack.

Problem-Awareincident-responsesocai-securityprompt-injectionforensicscontainment
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EU AI Act Implementation Timeline: What Triggers When Between February 2025 and August 2027

The EU AI Act entered into force August 1, 2024, but its obligations phase in across multiple dates between February 2025 and August 2027. The prohibited practices under Article 5 became enforceable on February 2, 2025. The general-purpose AI provider obligations under Articles 53 and 55 became enforceable August 2, 2025. The high-risk system obligations under Chapter III, Section 2 become enforceable August 2, 2026. The remaining obligations for high-risk systems already on the market follow on August 2, 2027. This article walks through each phase, the operational consequences for providers and deployers at each date, and the evidence each phase expects to find when a market surveillance authority inspects.

Compliance & Regulationeu-ai-actcompliancetimelineenforcementgpaihigh-risk-ai
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EU AI Act Deployer vs Provider: Who Owns Which Obligation in a High-Risk Deployment

The EU AI Act splits obligations between the provider that places an AI system on the market and the deployer that puts it into use. The split matters because deployers regularly assume they only have to consume the provider''s documentation, while providers regularly assume the deployer carries the runtime-evidence burden. Both assumptions leave gaps the regulator will surface. This article walks through the provider obligations under Articles 16, 17, and 43, the deployer obligations under Article 26, the shared traceability obligation under Article 12, and the operational division most enterprise deployments need to land before the August 2, 2026 enforcement date for high-risk systems.

Compliance & Regulationeu-ai-actcompliancedeployer-obligationsprovider-obligationshigh-risk-aiarticle-26
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DeepInspect vs Azure AI Content Safety: Independent Control Plane vs Microsoft-Only Coverage

Azure AI Content Safety is Microsoft''s native content-moderation service for AI workloads running on Azure OpenAI and Azure-hosted models. DeepInspect is a model-agnostic policy enforcement gateway that sits in front of any HTTP-based LLM, regardless of cloud. The two services answer different questions. Content Safety asks "is this content harmful for moderation purposes?" DeepInspect asks "does this specific identity, under this specific policy, get to send this specific request to this specific model right now?" This comparison covers what each service is, where each fits, the architectural differences, and how to think about combining them in a multi-cloud deployment.

Comparisons & Alternativescomparisonazure-content-safetyai-gatewaypolicy-enforcementmulti-cloud
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Prompts Become Shells: What Microsoft''s May Disclosure Means for Any Enterprise Running LangChain, AutoGen, or Semantic Kernel

On May 7, 2026, Microsoft Security Research published a disclosure that walks through prompt-to-shell escalation paths in mainstream AI agent frameworks, including LangChain, AutoGen, and Semantic Kernel. The disclosure reframes agentic AI from a data-leak concern into a remote code execution attack surface. The reframing matters because the SOC playbook for an RCE class of vulnerability is different from the privacy playbook most security teams currently apply to AI traffic. This article walks through the disclosed escalation paths, identifies which framework patterns are exposed, and outlines the enforcement architecture that contains the blast radius before the prompt reaches the agent.

Problem-Awareagentic-aircelangchainautogensemantic-kernelprompt-injection
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Colorado SB 26-189: Why HIPAA-Covered AI Deployers Lost Their Exemption

On May 14, 2026, Governor Jared Polis signed SB 26-189 into law, scaling back the Colorado AI Act ahead of its February 2026 effective date. The revised statute drops the broad HIPAA covered-entity exemption that the original act carried and replaces it with a narrower carve-out tied to a specific "consequential decision" test. Clinical AI deployers in Colorado who assumed they were out of scope now have to map the systems that influence diagnosis, treatment selection, or coverage decisions against the new criteria. The effective date moves to January 1, 2027, with a 60-day Attorney General cure period. This article walks through what changed, which clinical AI systems pick up new obligations, and the per-decision evidence the new regime will expect.

Industry Verticalscolorado-ai-acthealthcare-aihipaastate-regulationclinical-aicompliance
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What the EU Commission''s May 2026 High-Risk Classification Guidelines Change About Your AI Scope Assessment

On May 19, 2026, the European Commission published its draft guidelines clarifying which AI systems fall within the high-risk classification under Annex III of the EU AI Act. The guidelines arrive 75 days before the August 2 enforcement date for high-risk obligations. They tighten the criteria for "intended purpose," reshape how deployers and providers classify HR screening, clinical decision support, and fraud detection systems, and accelerate the scope assessment timeline. This article walks through the new criteria, applies them to three concrete enterprise deployments, and identifies the per-decision evidence each will need to produce on demand from August 2 onward.

Compliance & Regulationeu-ai-acthigh-risk-aicomplianceannex-iiiclassificationenforcement
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LLM Gateway: What It Is, Where It Sits, and What It Has to Enforce

An LLM gateway is a specialized proxy that sits between applications and LLM provider APIs. It handles model routing, rate limiting, retries, fallbacks, prompt classification, identity-aware policy enforcement, and audit logging. The category has split along two lines: traffic-management gateways that optimize cost and latency, and policy-enforcement gateways that operate as the compliance layer. The piece walks through what an LLM gateway is, where it sits architecturally, and what an enforcement-grade gateway has to produce.

AI Security Solutionsllm-gatewayai-gatewayarchitectureenforcementai-securitycompliance
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