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AI Security Solutions

97 posts on ai security solutions.

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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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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LLM Gateway: What It Is, Where It Sits, and What It Has to Enforce

An LLM gateway is a specialized proxy 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 category has split along two lines: traffic-management gateways that optimize cost and latency, and policy-enforcement gateways that operate as the compliance layer. The piece walks through what an LLM gateway is, where it sits architecturally, and what an enforcement-grade gateway has to produce.

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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.

ai-enforcementai-securityarchitectureai-gatewaycomplianceenforcement
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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 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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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 Traffic Inspection: The Layer Where Prompt Content Becomes Visible to the Enterprise Stack

AI traffic inspection is the layer where prompt content becomes visible to the enterprise control stack. Network telemetry sees AI endpoint reachability. CASB sees AI SaaS access. Endpoint DLP sees clipboard events. None of those layers reads the prompt body itself. AI traffic inspection sits at the AI request boundary and reads the structured JSON request and response, which is where the data actually moves. I walk through what the inspection point reads, where the existing telemetry is blind, and how the inspection point produces evidence for the 2026 compliance set.

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Sensitive Data AI Detection: Classifying Prompt Content at the AI Request Boundary

Sensitive data AI detection classifies prompt content at the AI request boundary, where the prompt is reconstructed into a structured payload and a classifier surfaces the categories the policy reads. The category set includes PII (email, phone, SSN, NPI), PHI, PCI, secrets (API keys, tokens, certificates), source code, and customer identifiers. Document-level classifiers do not run cleanly against prompt context windows. The inspection-point classifier runs at request time, surfaces labels the policy uses, and stamps the labels on the per-decision audit record.

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AI Policy Engine: Where the Decision Point Sits and What It Has to Evaluate

An AI policy engine evaluates every AI request against the deployer policy at the moment the request crosses the AI boundary. The engine reads identity context, prompt classification, model authorization, and policy state, then emits a pass or block verdict with a signed audit record. This article walks through what the engine has to evaluate, where it sits relative to the application and the model, and the architectural properties that make the engine defensible under audit.

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AI Security Incident Response: The Playbook Shape That Holds Up Under a Live AI Breach

An AI security incident response playbook covers the five phases of a live response (detection, containment, eradication, recovery, postmortem) adapted to the failure modes specific to AI: prompt-injected agents, model-leaked PII, tool misuse via the LLM, and shadow AI exfiltration. The playbook depends on a per-decision audit record stream the SOC can pull from in real time. This article walks through each phase, what the SOC needs from the runtime architecture, and the postmortem template that ties evidence back to the risk register.

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AI Egress Monitoring: The Outbound Inspection Layer Most Deployments Skip

AI egress monitoring inspects outbound traffic from the enterprise to LLM endpoints. The traffic carries prompt content, identity context, and the data classifications the deployer cares about. Most enterprise monitoring stops at the TLS encryption boundary and treats the AI traffic as a single egress destination. This article walks through what AI egress monitoring has to observe, the architectural patterns that produce visibility, and the operational signals that matter.

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