Blog

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

AI Vendor Due Diligence Questionnaire: What to Ask Before You Buy

AI vendor due diligence happens at the procurement gate, runs against a standard questionnaire, and produces an attestation file. The questionnaire most teams inherited from cloud SaaS vendors does not cover the questions a regulator will actually ask about AI use. The Fannie Mae LL-2026-04 framework, the EU AI Act, and the NIST AI RMF all expect ongoing due care, not one-time due diligence. This piece walks through the question categories an AI-aware procurement gate has to cover, the answers that have to live in the file, and the runtime evidence that closes the gap between due diligence and due care.

Compliance & Regulationai-governancevendor-riskcomplianceprocurementdue-diligenceaudit
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AI Gateway Multi-Tenant Isolation: Identity, Policy, and Audit at the Tenant Boundary

Multi-tenant AI deployments share infrastructure across tenants and have to enforce isolation at the request boundary. Tenant context attached at authentication time has to flow through every policy decision, every tool invocation, every retrieval call, and every audit record. A gateway that maintains the tenant boundary at all four touch points is the architectural pattern that keeps multi-tenant AI safe under load. This piece walks through where the tenant context has to land and what the audit record looks like when isolation holds.

Platform & Architectureai-gatewaymulti-tenantai-securityisolationpolicy-enforcementaudit
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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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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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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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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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Cisco-Astrix and the Rise of Identity-Aware AI Gateways

On May 4, 2026, SecurityWeek reported that Cisco moved to acquire Astrix Security for roughly $400M. The deal validates identity-aware AI traffic enforcement as a buying-center category. Non-human identities (NHIs) — API keys, OAuth tokens, agent identities — are the new entry point. This article walks through what the deal signals for the AI security stack, how NHI-platform-bolted-on approaches differ from inline policy enforcement at the LLM request boundary, and the RFP questions a CISO should ask before defaulting to a bundled offering.

Comparisons & Alternativesai-securityidentity-and-authorizationpolicy-enforcementai-governancearchitecturecybersecurity
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Mapping a Zero-Trust AI Gateway to NIST''s Upcoming COSAiS Single-Agent and Multi-Agent Overlays

NIST is teeing up the Concept of Operations for Securing AI Systems (COSAiS) overlays in two forms: a Single-Agent overlay and a Multi-Agent overlay, plus an AI RMF Profile for Critical Infrastructure. Federal contractors and critical infrastructure operators will be measured against these. The pre-map advantage is real: federal procurement reviews already reference the work in progress. This article walks the overlay structure, where a zero-trust AI gateway maps to each control family, and the evidence artifact each control consumes.

Platform & Architecturenistnist-ai-rmfzero-trustai-securitycompliancearchitecture
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Enterprise AI Governance: What the Operational Layer Actually Has to Produce

Enterprise AI governance gets framed as a policy program. The policies are necessary, but they sit on top of an operational layer that produces evidence, enforces controls, and tracks decisions in real time. This article walks through the four artifacts a real enterprise AI governance program needs at the operational layer: the AI system inventory, the per-decision audit record, the policy enforcement record, and the incident reconstruction artifact. Each is mapped to specific regulatory regimes and to the questions a board will ask.

Compliance & Regulationai-governanceenterprisecomplianceauditregulationai-security
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AI Bill of Materials (AIBOM): The Inventory Layer Compliance Teams Keep Skipping

Search interest in "AIBOM" and "AI bill of materials" is climbing fast, but the SERP is owned by vendors selling tooling rather than explainer content. This article defines AIBOM in concrete terms, compares it to the Software Bill of Materials (SBOM), maps the artifact to NIST AI RMF and EU AI Act Article 11 documentation requirements, and walks through what an AIBOM actually contains: model card references, training data lineage, inference dependencies, and gateway policy version. The per-decision audit log of LLM traffic is the inference-layer AIBOM artifact most programs are missing.

Compliance & Regulationai-governancecomplianceai-complianceauditeu-ai-actnist-ai-rmf
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Shadow AI in 2026: Detection Patterns, Real Incidents, and What Your SOC Should Already Be Doing

The shadow IT framing for shadow AI is now outdated. Shadow AI is browser-extension-deep: ChatGPT in DevTools, Copilot in IDE, Claude in Slack. Blocking fails for the same architectural reason it failed for shadow SaaS in 2018. This article walks through current detection patterns at the DNS, proxy, OAuth consent, and browser inventory layers, three documented shadow AI incidents from 2025-2026, and why a policy gateway succeeds where blocking does not. The piece refreshes the existing shadow AI canon for the patterns SOCs are actually seeing in production this year.

Problem-Awareshadow-aiai-securitycybersecuritydata-loss-preventiondlppolicy-enforcement
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Model Routing for Cost: What to Actually Measure Before Switching a Workload from GPT-4 to Haiku

Most "use the cheaper model" posts skip the rigor. Real model routing decisions have four layers: token cost, quality regression on an eval set, latency impact, and governance risk. This article walks through each layer with the questions a platform engineer should answer before flipping a workload from a frontier model to a smaller one, plus an example routing rule expressed at the gateway layer. The gateway is the right place to enforce routing because it has identity and policy context the application does not.

Platform & Architectureai-securitypolicy-enforcementarchitecturellmllm-securitydevsecops
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