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AI Security Solutions

97 posts on ai security solutions.

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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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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AI Gateway: The Architectural Component That Sits Between Calling Identities and LLM Endpoints

An AI gateway is the architectural component that sits between calling identities (users, agents, services) and LLM endpoints, terminates the AI provider TLS, evaluates identity-bound policy, applies a pass, redact, or block decision, commits a per-decision audit record, and forwards the request. The category covers four distinct shapes today: developer-tooling proxies, enterprise observability gateways, identity-aware enforcement gateways, and inference-side guardrails libraries. Only one of the four produces the audit record EU AI Act Article 12 reviewers accept.

ai-gatewayinline-enforcementai-policy-enforcementllm-securityaudit-logsai-control-plane
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LLM Proxy: The Architectural Pattern, the Operational Modes, and the Audit Record Each Mode Produces

An LLM proxy is a process that sits on the HTTP path between calling identities and LLM provider endpoints. The proxy can operate in three modes: pass-through observability, policy enforcement, or vendor multiplexing. The choice of mode decides what the audit record contains and whether the record satisfies regulatory expectations. A pass-through proxy logs the call. A policy enforcement proxy commits identity, classification, and policy state. A multiplexing proxy unifies the API across vendors. Regulated deployments typically need the enforcement mode.

llm-proxyai-gatewayinline-enforcementai-securityaudit-logsai-policy-enforcement
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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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LLM DLP: The Inspection Point Where Prompt Content Becomes Sensitive Data

LLM DLP is the inspection layer that catches PII, PHI, source code, and customer data inside the prompt body before it reaches an LLM endpoint. Network DLP, endpoint DLP, and email DLP each terminate inspection before the prompt is in scope. This piece walks through where each traditional layer stops, why the LLM request path slips through, the regulatory framing under EU AI Act Article 12 and HIPAA, and the architectural placement that produces a defensible per-request record.

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AI Data Classification: The Categories the Audit Record Has to Carry at the LLM Request Boundary

AI data classification is the layer that labels prompt content before policy evaluates and before the audit record commits. Deterministic categories for PII, PHI, source code, customer data, and free-form sensitive labels supply the field the EU AI Act Article 19 record expects on every decision. This piece walks through the categories, the placement where the classifier runs, the regulatory framing, and how the labels feed identity-bound policy at the request boundary.

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Prompt-Level DLP: Inspection at the Field Where the User Says What They Mean

Prompt-level DLP runs inspection at the prompt body sent to an LLM endpoint, not at file boundaries or network egress. The prompt is the data, and the prompt sits inside an encrypted POST body to a SaaS destination. This piece walks through where prompt-level DLP sits, the classifier categories it has to recognize, how the redaction decision feeds the model, and the regulatory framing under EU AI Act Article 12 and HIPAA.

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AI Prompt Redaction: The Substitution Step That Lets the Model Reason Without Touching the Raw Data

AI prompt redaction substitutes placeholders for sensitive content in the prompt before the model receives the request. The substitution preserves the structural cues the model needs to produce a coherent response while keeping the raw PII or PHI off the model provider. This piece walks through the redaction pattern, how placeholders feed the model, the audit record fields the redaction lands on, and the EU AI Act and HIPAA framing.

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AI API Gateway: What It Is, What It Does, and How It Differs from Traditional API Gateways

An AI API gateway is a specialized gateway that sits between applications and LLM provider APIs. It handles model routing, rate limiting, retries, fallbacks, prompt classification, identity-aware policy enforcement, and audit logging. The architecture differs from a traditional API gateway because the traffic it inspects is different: prompts and responses rather than structured API payloads. This piece walks through what an AI API gateway is, what it does, where it differs from traditional gateways, and what to evaluate when picking one.

ai-gatewayai-api-gatewayarchitectureenforcementcompliance
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ChatGPT DLP: Detecting and Preventing Sensitive Data in Prompts

ChatGPT DLP is the practice of detecting and preventing sensitive data from entering ChatGPT prompts. Traditional DLP operating at the email gateway, the storage layer, and the endpoint misses prompt-layer data movement. The architectural fix moves DLP into the AI request path with prompt-level classification, identity-aware policy, and per-decision audit records. This piece walks through where ChatGPT DLP needs to operate, what classifiers matter, and how it differs from network DLP that watches egress packets without prompt context.

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