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AI Security Solutions

39 posts on ai security solutions.

Identity-Aware AI Gateway: Why Per-User, Per-Role Policy Has to Live at the Request Boundary

An identity-aware AI gateway attaches the enterprise IdP identity to each AI request, evaluates per-user and per-role policy at the request boundary, and commits the audit record with identity context bound at decision time. The architecture differs from generic gateways that operate on application credentials only. The EU AI Act Article 19 identity-of-natural-persons requirement, the NIST agent identity framework, and the post-authentication gap each push the gateway to attach identity at the request rather than the session.

ai-gatewayidentityinline-enforcementai-policy-enforcementaudit-logscompliance
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Fail-Closed AI Gateway: Why the Default Has to Be Deny in Regulated Environments

A fail-closed AI gateway defaults to block when the policy decision is unreachable, when the classification result is uncertain, or when the gateway itself loses upstream connectivity. The opposite (fail-open) defaults to pass, which trades the regulatory record for availability. For high-risk AI under EU AI Act Article 12, DORA Article 19, and Fannie Mae LL-2026-04, the regulatory posture only holds under a fail-closed default. The architectural cost is operational investment in availability; the regulatory cost of fail-open is the loss of the contemporaneous record at exactly the moment a regulator would ask for it.

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AI Policy Enforcement: Where the Decision Happens and the Record That Survives Review

AI policy enforcement has to operate at a specific layer in the request path to produce a record that survives an EU AI Act Article 12 review. Most stacks place the enforcement inside the application that makes the AI call, which fails the traceability test. This piece walks through where the enforcement has to sit, the properties the layer must carry (deterministic, identity-aware, fail-closed, sub-50ms), the record series the layer commits, and the regulatory framing that makes the placement non-optional.

ai-policy-enforcementai-gatewayinline-enforcementeu-ai-actaudit-logsai-security
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AI DLP: Why Traditional Data Loss Prevention Misses the LLM Request Path and What Replaces It

Traditional DLP sits at the network edge or endpoint and inspects files and email. AI DLP has to sit at the HTTP request layer between authenticated users or agents and the LLM endpoint, because the prompt is the data and the prompt is inside an encrypted POST body the network DLP never sees. This piece walks through where each DLP layer terminates inspection, the regulatory framing under EU AI Act Article 12 and HIPAA, and the inspection architecture that produces a defensible record.

ai-dlpllm-dlpdata-loss-preventionai-securityinline-enforcementeu-ai-act
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AI Control Plane: What Sits at the Request Boundary and What an Auditor Reviews

The phrase "AI control plane" gets applied to four different layers in the stack. Each layer has a different inspection target, a different enforcement timing, and a different audit record. This piece walks through what an AI control plane has to do at the HTTP boundary between authenticated users or agents and the LLM, where most candidate products fall short of EU AI Act Article 12 review, and the record series the inspection layer commits at decision time.

ai-control-planeai-gatewayinline-enforcementai-policy-enforcementaudit-logseu-ai-act
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LLM Egress Control: The Per-Request Identity, Classification, and Audit Layer for AI Provider Traffic

LLM egress control is the request-time enforcement layer between corporate applications (and agents) and the external LLM endpoints they call. The layer reads the identity the request carries, classifies the prompt body, evaluates per-route policy, applies a pass, modify, redact, or block decision, and commits a per-decision audit record. This piece walks through the egress surface the layer covers, the policy decisions the layer commits, the audit record format, and the deployment topology that handles single-region and multi-region traffic.

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Zero Trust Applied to AI Systems: The Per-Request Identity, Policy, and Audit Boundary

Zero-trust architecture replaces the perimeter assumption with per-request verification of identity, device, and policy. Applied to AI systems, the same principle moves the verification to the AI request boundary: who is making the call, what classification the request carries, what policy version evaluates the call, and what audit record the layer commits. This piece walks through the four zero-trust principles and how each one maps to a concrete decision the AI request path has to commit on every call.

zero-trustai-securityidentity-awareaudit-logsinline-enforcementpolicy-enforcement
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AI Agents vs RPA: How the Security Model Changes When the Bot Reasons Before It Acts

RPA bots execute deterministic scripts under a service account. AI agents read context, plan multi-step actions, and call tools whose return values shape the next step. The security model that worked for RPA (network segmentation, credential vaulting, scheduled execution) breaks when the bot reasons before it acts. This piece walks through the four architectural differences between RPA and AI agents, the new attack surfaces the reasoning step introduces, the identity-aware enforcement the deployment owes, and the audit record format that survives a regulator review.

ai-agentsrpaagent-securityidentity-awareaudit-logsagentic-ai
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AI Prompt Risk Scanner: A Free Tool To Check What Your AI Prompts Actually Expose

The AI Prompt Risk Scanner is a free tool that inspects a sample of your organization prompts against the same detection rules a production inspection layer would apply. Paste a prompt or upload a batch, and the scanner returns the data classes detected, the regulatory exposures triggered, and the policy outcomes that would fire under standard rules. This piece walks through what the scanner inspects, how the rules work, and what to do with the results.

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AWS Bedrock Guardrails Architecture Deep Dive: Where the Inspection Sits and What It Cannot See

AWS Bedrock Guardrails sit inside the model invocation path on the AWS side of the API boundary. The architecture covers AWS-hosted endpoints with policies AWS authors and evaluates. This piece walks through the Bedrock Guardrails request path, the four policy categories AWS exposes, where the inspection actually runs, the audit records the deployer receives, and the deployment patterns the Bedrock-only customer and the multi-cloud customer should each consider.

aws-bedrockbedrock-guardrailsai-securityai-architectureinline-enforcementaudit-logs
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AI Prompt Risk Scanner: A Free Tool to Check Prompts for PII, PHI, Secrets, and Injection Patterns

Most production AI applications send prompts to vendor LLM endpoints without an inspection layer. The prompt content carries PII, PHI, secrets, and prompt-injection vectors at rates the application teams underestimate. This page walks through the free prompt risk scanner the DeepInspect team built, the four classifiers it runs, and the report format that tells you what your traffic actually carries.

prompt-riskprompt-scannerpii-detectionphi-detectionprompt-injectionfree-tool
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Setting Up AI Policy Enforcement: From the First Rule to a Production Deployment

AI policy enforcement is the runtime control point that turns a written policy into a per-request decision. This guide walks through how to set up enforcement: the policy schema, the decision-point placement, the per-route and per-role rules, the audit format that proves the policy was applied, and the deployment sequence that gets a production-ready enforcement layer live in 8 to 12 weeks.

ai-policyenforcementimplementation-guideai-governanceinline-policyai-security
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