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

GDPR AI DPIA: When Article 35 Requires a Data Protection Impact Assessment for an AI System

GDPR Article 35 requires a Data Protection Impact Assessment when processing is likely to result in a high risk to the rights and freedoms of natural persons. Deploying an LLM against personal data almost always triggers the Article 35 threshold under the criteria the Article 29 Working Party and the European Data Protection Board have published. This piece walks through the Article 35 mandatory triggers, the EDPB Guidelines 3/2019 signals that apply to AI systems, the DPIA process steps under Article 35(7), the coordination with the EU AI Act Article 27 Fundamental Rights Impact Assessment, and the inspection-layer records that the DPIA references for the ongoing monitoring under Article 35(11).

Compliance & Regulationgdprdpiaarticle-35privacyai-complianceedpb
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AI Usage Policy Template: The Clauses That Actually Get Enforced at the Gateway

Most AI usage policies get written as documents and stored in a compliance drive. The document alone changes no request that leaves the employee's browser and reaches ChatGPT, Claude, or a shadow copilot. The clauses in this template are the ones that map to enforcement at the AI request layer, where a policy statement translates into a permit-or-deny decision on live traffic. The template covers scope, sanctioned providers, data classes prohibited from AI prompts, allowed use cases per role, monitoring, incident reporting, and the enforcement mechanism that binds the policy to the traffic. Adopt the template as the policy artifact, then wire the clauses to the gateway that produces the audit records the policy owner samples at quarterly review.

Problem-Awareshadow-aiai-policypolicy-templateai-usage-policygovernanceciso
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EU AI Act General-Purpose AI: Article 53 Obligations, the August 2 Deadline, and the Deployer Consequences

The EU AI Act separates general-purpose AI (GPAI) rules from the high-risk system rules. GPAI obligations under Articles 53 through 55 sit with the model providers (OpenAI, Anthropic, Google, Mistral, Meta) and take effect August 2, 2026. Downstream deployers absorb second-order obligations through the technical documentation and evaluation records upstream providers must supply. This piece walks through what Article 53 requires from GPAI providers, what the systemic-risk threshold under Article 55 changes for the frontier labs, and the practical inspection-layer records a deployer running GPT-5, Claude 4, or Gemini 3 needs to keep against Articles 12, 13, and 26 in parallel.

Compliance & Regulationeu-ai-actgpaigeneral-purpose-aiarticle-53article-55compliance
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AI Agent Permission Escalation: Five Patterns That Promote an Agent Past Its Authorized Scope

When an AI agent makes calls that exceed its authorized scope, the call path crosses a gateway, an LLM, and downstream services. Escalation can occur at any boundary in the chain. The pattern is rarely a single exploit; the pattern is the agent stitching together several legitimate primitives into a chain that produces an outcome the deployer did not authorize. This article walks five escalation patterns observed in production, the gateway signals that catch each, and the policy structure that prevents the chain from completing even when the model is induced to attempt it.

Problem-Awareagentic-aipermission-escalationagent-securityauthorizationai-securitypolicy-enforcement
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NIST AI RMF MEASURE Function: The Controls That Produce Auditable Evidence

The NIST AI Risk Management Framework organizes risk management into four functions: GOVERN, MAP, MEASURE, and MANAGE. MEASURE is the function that produces the operational evidence the other three functions depend on. The framework defines four categories under MEASURE, with 18 subcategories that specify what to assess and how to assess it. This article walks each category, the controls a deployer needs in production to satisfy them, the artifacts the controls produce, and where a stateless policy gateway sits in the evidence chain.

Compliance & Regulationnist-ai-rmfcompliancerisk-managementmeasurementauditai-governance
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AI Agent Context Window Poisoning: How a Single Bad Retrieval Steers an Entire Session

An AI agent runs in a context window: the system prompt, the user request, the retrieved documents, the prior tool calls, and the prior model responses. The window is the model''s working memory for the session. Context window poisoning is the attack pattern where attacker-controlled content lands in the window and steers the model''s subsequent decisions. A single bad retrieval can alter the model''s behavior for the rest of the session. This article walks the attack vectors, the detection signals at the gateway, the redaction patterns that prevent the poison from reaching the model, and the audit record that supports investigation.

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Prompt Injection via MCP Tool Descriptions: The Attack Surface in the Schema Itself

When a client connects to a Model Context Protocol server, the server advertises its tools to the model through descriptions. The model reads the descriptions to decide which tool to call. A malicious MCP server can place prompt-injection content in the tool descriptions themselves. The model treats the description as instructions, not as data. The attack surface lives inside the schema that the protocol uses to advertise its capabilities. This article walks the attack pattern, the variants that have surfaced, the detection signals, and the gateway controls that contain the blast radius.

Problem-Awaremcpprompt-injectionagent-securityai-securitytool-useagentic-ai
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AI Gateway Multi-Region Failover: The Architecture That Survives a Regional LLM Outage

A regional LLM provider outage takes down every AI feature that depends on that region. The mitigation is a gateway architecture that routes around the failure within seconds. Multi-region failover at the AI gateway has three components: a gateway deployment in at least two regions, a policy and routing layer that supports per-region destinations, and a health-aware traffic director that promotes a region to active when the primary fails. This article walks the architecture, the failure modes that recur, the audit-log implications across regions, and the operational drill.

Platform & Architectureai-gatewaymulti-regionfailoverhigh-availabilitydisaster-recoverydevops
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AI Gateway Rollback Strategy: How to Revert a Policy or Model Change Without Breaking the Audit Trail

A bad policy change or a broken model upgrade at the AI gateway has to be reverted fast. The rollback is the high-availability move that prevents a small problem from becoming a service-wide outage. The rollback also has to preserve the audit trail, because the regulatory record of "what policy was in effect when" survives the rollback. This article walks the rollback patterns that work at the gateway layer, the failure modes that catch teams off guard, the integrity controls that keep the audit record consistent across the revert, and the operational drill that proves the rollback works before it has to.

Platform & Architectureai-gatewayrollbackdeploymentpolicy-versioningaudit-logginghigh-availability
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AI Gateway Blue-Green Deployment: How to Ship a Gateway Version Without Cutting Traffic

A blue-green deployment runs two full gateway environments in parallel, with traffic flipped at a load balancer from the current (blue) environment to the new (green) environment after the green environment has been verified. The pattern works for AI gateways with two differences from a standard API gateway: the policy and routing state has to be consistent across the cutover, and the audit log chain has to remain unbroken. This article walks the blue-green pattern at the AI gateway layer, the state-consistency requirements, the verification gates, and the fallback path.

Platform & Architectureai-gatewayblue-green-deploymenthigh-availabilitydeploymentaudit-loggingdevops
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EU AI Act Conformity Assessment Bodies: Which Notified Bodies Will Sign Off Your High-Risk System

The EU AI Act requires high-risk AI systems to undergo a conformity assessment before being placed on the market. For some categories, the provider self-assesses. For others, the provider has to engage a notified body that the member state has designated under Article 31. With August 2, 2026 thirty-two days away, providers need a working understanding of which Annex III categories trigger third-party conformity assessment, how the notified body designation process works, and what the assessment record looks like when it lands in a market surveillance investigation.

Compliance & Regulationeu-ai-actconformity-assessmentnotified-bodiescompliancehigh-risk-airegulation
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Serious Incident or Malfunction: The Article 73 Trigger That Decides Whether the Clock Starts

The EU AI Act Article 73 reporting obligation hinges on whether an event qualifies as a serious incident under the Article 3(49) definition. Operationally, the difference between a serious incident and an internal malfunction is the difference between a 15-day external reporting clock and an internal incident review. The provider that misclassifies a serious incident as a malfunction has missed the regulatory window. This article walks the Article 3(49) definition, the decision criteria the supervisory authorities apply, the borderline case patterns that recur in enterprise deployments, and the operational record the triage decision requires.

Compliance & Regulationeu-ai-actincident-responsecompliancearticle-73high-risk-airegulation
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