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

Indirect Prompt Injection: How RAG and Tool-Use Pipelines Get Compromised Through Retrieved Content

Indirect prompt injection is the attack pattern where adversarial content reaches the model through a retrieved document, a tool result, or any other source the model treats as part of its context. The attacker never interacts with the application directly. The injection succeeds when the model executes the embedded instructions on the next retrieval or the next agent loop iteration. RAG pipelines and tool-using agents are exposed by construction. This piece walks through the attack mechanics, the surface area in production deployments, why the model alone cannot defend, and the request-boundary controls that produce a defensible posture.

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Jailbreaking LLMs: What the Attack Looks Like in Production and the Request-Boundary Defense That Holds Up

Jailbreaking is the class of attacks where adversarial prompts cause the model to disregard the safety training and produce content the provider intended to suppress. The attack catalog spans role-play framing, multi-step persuasion, encoded payloads, and the fine-tuning bypass that targets the refusal patterns directly. Stanford Trustworthy AI and the AIUC-1 Consortium research found that refusal behaviors degrade significantly under adversarial pressure. This piece walks through the attack patterns in production, why the model alone cannot defend, and the request-boundary controls and audit record format that produce a defensible posture.

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OWASP LLM Top 10: How the 2025 Update Maps to Production AI Security Controls

The OWASP LLM Top 10 enumerates the application-security risks that show up when an LLM is wired into a production application. The 2025 update reorganized the list to reflect what production teams actually see: prompt injection at the top, sensitive information disclosure and supply chain risk close behind, and a new category for unbounded resource consumption. This piece walks each risk to the inspection layer control that produces a defensible posture, the gap each risk exposes in standard application-side defenses, and where the audit record series intersects EU AI Act Article 12 and DORA Article 19 evidence obligations.

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Prompt Injection Examples: 12 Real Patterns From Production Incidents and the Inspection Layer Response

Prompt injection examples that surface in production AI systems follow a small number of repeatable patterns. The patterns appear across customer support agents, RAG pipelines, agentic browsers, and code-assist tools. Each pattern has a control point at the request boundary where an inspection layer can produce a deterministic signal the policy can act on. This piece walks through twelve patterns from production incident response, the injection text that triggers each, the inspection-layer response that holds up, and the audit record that supports the post-incident review.

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AI Audit Logs: The Format Spec That Survives EU AI Act, DORA, and Fannie Mae Review

AI audit logs that survive regulatory review carry a specific set of fields the EU AI Act Article 12, DORA Article 19, Fannie Mae LL-2026-04, NIST AI RMF, and HIPAA all expect on the same record. The fields cover identity, decision provenance, model identity, policy state, and integrity metadata. The format has to support per-record retrieval and per-series replay. The write path has to sit outside the application so the application cannot modify the record. This piece walks through the field-level format specification, the integrity model, the storage characteristics, and the deployment pattern that produces records the regulator and the customer auditor will accept.

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LLM Audit Logging: The Implementation Pattern That Holds Up Under Regulator Review

LLM audit logging implementations split along three architectural patterns: in-application logs, sidecar collectors, and inline inspection layers. The inline pattern is the only one that produces records the EU AI Act Article 12, DORA Article 19, and Fannie Mae LL-2026-04 reviewers accept because it is the only one that satisfies the write-path independence test. This piece walks through the three patterns, the architectural reason the first two fall short, the integration points the inline pattern requires, the field set the records have to carry, and the latency budget that fits a production deployment.

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GDPR and AI: Where Article 5, Article 22, and Article 32 Reach Production AI Deployments

GDPR applies to AI deployments wherever the AI system processes personal data of EU residents. The applicable articles overlap with the EU AI Act but predate it and reach a broader surface. Article 5 imposes the lawfulness, purpose limitation, and data minimization principles. Article 22 limits automated individual decision-making. Article 32 imposes the security of processing obligation that the audit log is evidence against. This piece walks through the GDPR articles that reach production AI deployments, the specific obligations each creates, where most AI implementations fail the test, and the inspection-layer architecture that produces the evidence the data protection authority will accept.

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GDPR Article 22 and AI: What Automated Decision-Making Requires of Production Deployments

GDPR Article 22 limits decisions based solely on automated processing that produce legal or similarly significant effects on the data subject. AI deployments that produce loan approvals, credit decisions, hiring decisions, fraud-detection outcomes, or insurance underwriting fall inside the scope. The exemption pathways carry their own obligations: explicit consent, contract necessity, or Union or member state authorization. The Article 22(3) right to obtain human intervention and the transparency obligation require records that demonstrate the meaningful intervention happened and that the data subject received meaningful information. This piece walks through the article, the exemption pathways, the meaningful-intervention test, and the inspection-layer architecture that produces the evidence the supervisor will accept.

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PCI DSS and AI: How v4.0 Reaches Production AI Deployments Touching Cardholder Data

PCI DSS v4.0 took full effect on March 31, 2025. The standard reaches AI deployments wherever cardholder data passes through an AI prompt, a tool result, or a retrieval corpus the AI system queries. The applicable requirements include the data flow documentation under Requirement 1, the cardholder data discovery and scope reduction under Requirement 3, the access control restrictions under Requirement 7, the logging obligations under Requirement 10, and the security testing obligations under Requirement 11. This piece walks through the requirements that reach AI deployments, where most implementations fail the QSA review, and the inspection-layer architecture that produces the audit evidence and the scope reduction the assessor will accept.

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EU AI Act for Credit Scoring: Annex III Classification and Article 12 Logging

Annex III, point 5(b) of the EU AI Act classifies AI used to evaluate the creditworthiness of natural persons or establish their credit score as high-risk. The classification can trigger Article 12 logging, Article 13 transparency, Article 14 human oversight, and Article 26 deployer obligations. Regulation (EU) 2026/1744 moved the Annex III application date to 2 December 2027. This piece explains the classification, the evidence a credit-scoring deployment needs, and the limits of an HTTP AI gateway.

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OpenAI Enterprise Security: The Admin Controls and the Layer Above Them

OpenAI gives enterprise buyers two different surfaces with two different control sets: ChatGPT Enterprise for people and the OpenAI API for applications and agents. Both offer SAML SSO, SCIM, no-training defaults, and a compliance and audit surface. Those controls govern accounts and the vendor relationship, and stop at whether a specific authenticated caller may send a specific prompt to a model. This piece walks the controls on both surfaces, marks where they stop, and gives the identity-aware authorization pattern that closes the post-authentication gap on OpenAI traffic, with request-level detail.

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EU AI Act Implementation Timeline: What Triggers When Between February 2025 and August 2028

The EU AI Act entered into force August 1, 2024, but its obligations phase in across multiple dates between February 2025 and August 2028. 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 Act itself applies from August 2, 2026, including the Article 50 transparency duties. Regulation (EU) 2026/1744 then deferred the Chapter III high-risk obligations to December 2, 2027 for Annex III systems and August 2, 2028 for Annex I systems embedded in regulated products. 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.

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