Blog

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

EU AI Act Providers vs Deployers: Who Owns Article 16 vs Article 26 Obligations

The EU AI Act assigns different obligations to providers and deployers of high-risk AI systems. Article 16 covers provider obligations; Article 26 covers deployer obligations. The split matters because most enterprises operating AI in the EU are deployers, not providers, and the deployer obligations are routinely underestimated. The Digital Omnibus on AI (Regulation (EU) 2026/1744) deferred standalone Annex III high-risk obligations from August 2, 2026 to December 2, 2027, and embedded Annex I systems to August 2, 2028, but the provider-deployer split itself did not change. This article walks the provider-deployer split, the cases that change the assignment, and the architectural artifacts each side needs.

Compliance & Regulationeu-ai-actcomplianceai-governanceregulationhigh-risk-aideployer-obligations
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AI Gateway Architecture: The Components That Sit Between an Enterprise Caller and an LLM Endpoint

An AI gateway architecture has six core components: TLS termination, identity binding, request inspection, policy evaluation, the model router, and the audit record emitter. Each component is a placement decision that ties to a regulatory obligation or an operational property. This piece walks through the components, the placement decisions, and how the gateway integrates with the corporate IdP and the SIEM.

Platform & Architectureai-gateway-architectureai-gatewayai-securityinline-enforcementaudit-logs
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OpenAI Usage Tier Controls: How an Enterprise Enforces Per-Team Budgets on the Same API Key

OpenAI's account usage tiers describe the account-level rate ceiling. The tier is a single number the account holds, and the tier does not describe the enterprise's per-team, per-application, or per-user budgets. An enterprise that runs OpenAI at scale has to enforce a set of budget controls that sit above the account tier. This piece walks through the pattern set: per-team token budgets, per-application spend caps, per-user rate ceilings, and the audit records that tie every request back to the team accountable for it.

Platform & Architectureopenaiusage-tierai-costai-gatewaybudget-controlai-security
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LLM Audit Log Retention: SOX, HIPAA, GDPR, EU AI Act

Standard LLM and application logs do not satisfy SOX, HIPAA, or GDPR audit requirements on their own. They lack the identity claim, the classification outcome, and the policy decision an auditor asks for. The retention period on top of that depends on which regulation applies: the EU AI Act Article 19 floor is six months, HIPAA sets 6 years, SOX sets 7 years, GDPR ties retention to purpose necessity, and FINRA sets 6 years on communications. This piece walks through each regulation's actual rule, the maximum-of-applicable-floors approach most compliance teams end up using, and the tamper-evident storage properties the records need to survive the retention period.

Problem-Awareai-audit-logslog-retentioncomplianceeu-ai-acthipaasox
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DeepInspect vs CalypsoAI: Runtime Enforcement vs Model Evaluation

CalypsoAI, now part of F5 after a September 2025 acquisition, evaluates and red-teams AI models before and during deployment. DeepInspect enforces identity-bound policy on every live production request across any LLM provider. This comparison lays out where model evaluation ends and per-request runtime enforcement begins, and when a security team needs both.

Comparisons & Alternativesai-securityllm-securityai-governancezero-trustpolicy-enforcementarchitecture
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DeepInspect vs BigID: Live AI Traffic Enforcement Versus Data-at-Rest Discovery

BigID discovers and classifies sensitive data across structured and unstructured repositories, cloud storage, and SaaS applications on a scheduled scan, supporting GDPR and CCPA compliance and data security posture management. DeepInspect is a stateless, identity-aware proxy that inspects live HTTP traffic between authenticated users or agents and any LLM, enforcing policy per request and producing signed audit records. This piece compares the two architectures.

Comparisons & Alternativesdata-loss-preventiondlpai-governanceai-securityzero-trustcompliance
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CalypsoAI Alternatives: Model Evaluation vs Inline Policy Enforcement

CalypsoAI is built for red-teaming and model evaluation, testing model behavior on a scheduled cadence rather than deciding on individual live requests. This piece separates that job from five adjacent categories, including runtime guardrail libraries, cloud-native model guardrails, and identity-aware inline enforcement gateways, with an honest best-for for each, including where DeepInspect fits.

Comparisons & Alternativesai-securityllm-securityai-governancezero-trustpolicy-enforcementidentity-and-authorizationarchitecture
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Tool Poisoning: The Tool Description as an Attack Payload

Tool poisoning hides an instruction inside a tool's name, description, or parameter schema instead of the user's prompt. Agent applications serialize every available tool into the same HTTP request sent to the LLM, so the instruction reaches the model as trusted context, on a request an inline gateway can inspect. This piece defines the mechanism, distinguishes it from the cross-agent privilege escalation research covered elsewhere on this blog, and states where gateway controls stop.

Problem-Awareai-securityagentic-aiprompt-injectionllm-securityzero-trust
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PII Redaction in LLM Traffic Has to Happen at the Request, Not the Log

PII redaction has to run on the live HTTP request and response between a user or agent and an LLM, not on a periodic scan of stored documents. This piece breaks down the four paths PII takes into model traffic, why document-level tools miss all of them, and what a redaction policy needs to specify to hold up under review.

Problem-Awareai-securityllm-securitydata-loss-preventionpolicy-enforcementauditzero-trust
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AI Tenant Isolation: How Multi-Tenant SaaS Enforces Per-Customer Boundaries on LLM Traffic

Multi-tenant SaaS applications that add LLM features carry a new isolation obligation on top of the database and storage isolation the platform already enforces. Prompts flow through the LLM provider carrying tenant-specific data. Retrieval-augmented generation queries the vector store where tenant data lives. Agent tools call downstream systems that hold tenant data. Each of these paths introduces a way for tenant A's data to reach tenant B's context without a database join between them. This piece walks through the four isolation domains (prompt, retrieval, tool call, response), the enforcement patterns at the AI gateway, and the audit records that demonstrate the isolation held across the audit period.

Platform & Architecturemulti-tenantai-securitytenant-isolationsaasragai-gateway
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Model Theft Through APIs: Authorization and Audit Controls

Model theft through an API is an extraction and abuse problem that travels through ordinary authenticated requests. Model authorization, request budgets, response policies, and per-decision records give a platform team a control surface before the model returns data.

Problem-Awareai-securityllm-securitypolicy-enforcementauditzero-trust
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