← All posts

AI Security Solutions

44 posts on ai security solutions.

LLM Response Content Filter: The Transform Patterns That Convert an Unsafe Answer Into a Safe One Without Blocking the Request

Blocking every unsafe response is the wrong default for many production deployments. A well-scoped response filter transforms the unsafe portion (redacts PII, rewrites competitor mentions, strips prompt-injection payloads intended for downstream systems) and passes the safe remainder through. This covers the transform patterns, where they sit in the streaming response path, and how the audit record differentiates a transform from a block.

llm-response-filterai-securitycontent-filterllm-dlpai-gatewayoutput-filtering
Read post →

AI Firewall vs AI Gateway vs AI Proxy: The Category Distinctions Buying Teams Keep Blending Together

The three terms describe overlapping but distinct control points on AI traffic. AI firewall filters prompts and responses for policy violations. AI gateway aggregates traffic across LLM providers with rate limiting and routing. AI proxy is the transport layer that inspects the HTTP session. The buying decision turns on which control you actually need at what latency budget, and where identity binding happens in the request path.

ai-gatewayai-firewallai-proxyai-securityarchitectureai-policy-enforcement
Read post →

AI usage quota enforcement: the four counters production deployments actually need

AI usage quota enforcement is the mechanism that keeps AI spend, provider rate limits, and cross-tenant fairness under control. Production deployments need four counters at the gateway: per-caller request rate, per-tenant token throughput, per-workload cost, and per-model concurrency. Each counter answers a different failure mode. This piece walks through the four counters, where each one sits in the request flow, the fail-closed behavior each one demands, and the audit fields the enforcement decisions produce.

ai-quotarate-limitingai-gatewaycost-controlai-policy-enforcement
Read post →

LLM Response Redaction Patterns: How to Filter Model Output Without Breaking the Response

The prompt is the input the gateway inspects before the model sees it. The response is the output the gateway inspects before the caller sees it. Response redaction runs against free-form generated text, which is a harder inspection problem than prompt classification. This piece walks through the redaction patterns that hold up on the response side: token-boundary preservation, semantic-preserving substitution, structured-response filtering, and the audit records that prove the filter ran. The patterns apply to the LLM DLP layer of any inline gateway.

llm-dlpresponse-redactionai-securityai-dlpai-gatewaydata-leak-prevention
Read post →

LLM Egress Monitoring: Inspecting the Prompt at the Boundary Before It Reaches the Model Provider

Traditional egress monitoring inspects outbound network traffic against a network-DLP catalog. The catalog was designed for file transfers, email attachments, and web form submissions. LLM prompts leave the enterprise as HTTPS request bodies to api.openai.com, api.anthropic.com, and the Bedrock and Vertex endpoints. The network DLP inspects the header but cannot inspect the body when the body is TLS-encrypted. Even where a proxy terminates TLS, the DLP pattern set does not recognize prompt content the way it recognizes credit card numbers or file signatures. This piece walks through the failure modes, the inspection-layer architecture, and the enforcement decisions the layer supports.

ai-dlpllm-dlpegress-monitoringnetwork-securityshadow-ai
Read post →

AI Tool Call Policy Enforcement: Why the Tool Surface Is the Real Attack Surface

A chatbot that only generates text has a small attack surface. The same chatbot wired to ten tool functions that read files, query databases, and call external APIs has the attack surface of those ten functions plus the model that decides when to call them. AI tool call policy enforcement evaluates each function invocation against the identity that triggered it, the data classification of its arguments, and the policy version in force. This walkthrough covers the boundary where the gateway sees tool calls, the rules that scale across hundreds of functions, and the audit record per invocation.

tool-callsai-gatewayfunction-callingpolicy-enforcementagentic-ai
Read post →

MCP Policy Enforcement: Treating Model Context Protocol Calls Like AI Traffic, Not Plugin Traffic

Model Context Protocol turned a model conversation into a fan-out of tool calls and resource reads against external systems. Each fan-out arm now carries its own identity, its own scope, and its own data classification, and the policy plane has to evaluate every arm before the model receives the result. MCP policy enforcement sits at the boundary where the agent reaches the MCP server, inspects the call as AI traffic rather than plugin traffic, and produces an audit record per tool invocation. This walkthrough covers the boundary, the policy fields a Principal Engineer expects to see, and the audit fields the regulator expects.

mcpai-gatewaytool-callspolicy-enforcementagentic-ai
Read post →

AI Policy Versioning: Why the Audit Record Has to Carry a Policy Version, Not Just a Decision

A per-decision audit record without a policy version is a decision without a rule. When the regulator asks why the model was allowed to produce the response, the answer requires the exact rule set in force at the moment of the decision. AI policy versioning treats the policy plane as code, attaches a version identifier to every decision, and stores the policy text the version refers to in a registry the audit pipeline can reach. This walkthrough covers the versioning scheme, the rollout patterns that survive contention, and the audit-record fields the regulator expects.

policy-versioningai-gatewayauditgovernance-as-codecompliance
Read post →

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.

llm-gatewayai-gatewayarchitectureenforcementai-securitycompliance
Read post →

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
Read post →

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
Read post →

Azure AI Content Safety Architecture Deep Dive: Where the Inspection Sits and What It Cannot See

Azure AI Content Safety runs inside the Azure-hosted classification path. The product covers text, image, prompt-shield, groundedness, and protected-material checks the deployer composes through the Content Safety endpoint. This piece walks through the request path, the API surfaces, the policy categories, the audit records the deployer receives through Azure Monitor and the Foundry observability stack, and the deployment patterns the Azure-only customer and the multi-cloud customer should each consider.

azure-aicontent-safetyai-securityai-architectureinline-enforcementaudit-logs
Read post →