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AI Security Solutions

39 posts on ai security solutions.

LLM DLP vs Traditional DLP: Why the Two Controls Operate on Different Data Channels

Traditional DLP inspects file movements, email egress, and known data shapes on the network. LLM DLP inspects prompt content and model responses at the AI request boundary. The two controls operate on different data channels and produce different evidence. I walk through what each control sees, where each one is blind, and why the EU AI Act Article 12 obligations require a control at the LLM request layer that traditional DLP architectures cannot satisfy.

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AI Response Redaction: The Return-Path Inspection Step Most LLM Deployments Skip

AI response redaction inspects the model output before it reaches the caller and rewrites or blocks any segment that fails policy. The return path matters because LLMs reconstruct sensitive content from training data, retrieve PHI or PII from connected stores, and generate prohibited disclosures even when the prompt was clean. I walk through where response redaction sits in the AI gateway pattern, what the policy decision actually evaluates, and how it satisfies EU AI Act Article 12 and the NIST AI RMF Measure function.

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Per-Role AI Policies: How to Operationalize Identity-Bound AI Authorization

Per-role AI policies authorize what a user can do with AI based on the role the user holds inside the deployer organization. The policy expresses which models a role can call, which data classifications the role can include in prompts, which destinations and actions the role can target, and what oversight applies. The pattern is the AI extension of the role-based access control model the rest of the enterprise security stack already operates. The piece walks through what a per-role AI policy actually contains, how it propagates through the request path, and where it satisfies the regulatory authorization requirements.

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AI Inline Enforcement: The Architectural Pattern Compliance Frameworks Assume

AI inline enforcement is the architectural pattern where policy decisions on AI traffic happen at the moment of the request, in the request path, before the prompt reaches the model. The pattern contrasts with post-hoc detection that observes traffic after the fact and out-of-band approval flows that gate AI usage at provisioning time. The 2026 compliance frameworks, the 22-second median attacker handoff time, and the per-decision audit obligation all assume inline enforcement is the operating layer. The piece walks through what inline means, what it produces, and why the alternatives fall short.

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PII Detection in LLM Prompts: Classifier Choices and the Per-Request Decision

PII detection on LLM prompts has to operate at request latency, work on free-form text, and produce a deterministic classification that drives a policy decision. The classifier choices fall into three categories: regex and lookup tables, small purpose-trained models, and LLM-based classifiers. Each has a latency and coverage profile. This piece walks through the choices, where each fits, the integration into the AI request boundary, and the audit record the classification produces.

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Copilot DLP: Inspecting What Microsoft Copilot Sends to the Model

Copilot DLP is the practice of detecting and preventing sensitive data movement through Microsoft Copilot, GitHub Copilot, and the broader Copilot product family. The Copilot products operate inside enterprise workflows where confidential data is the default content. Traditional DLP at the email gateway, the endpoint, and the network layer misses the prompt-content movement. This piece walks through where Copilot DLP needs to operate, what classifiers matter for the Copilot data surfaces, and how the per-request audit record satisfies the Article 12 disclosure obligation.

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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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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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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 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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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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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.

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