What is Highflame

Highflame — security infrastructure for AI agents. Runtime guardrails with sub-10ms latency, Cedar policy enforcement, agent identity, and adversarial red teaming.

Highflame is security infrastructure for AI agents and autonomous systems. It provides a shared control plane for defining what agents are allowed to do, enforcing those rules at runtime, observing behavior across products and environments, testing systems adversarially, and producing an audit trail for what happened.

If your organization is deploying agents that call tools, connect to MCP servers, modify code, invoke APIs, or operate across long-running workflows, Highflame is the layer that makes that autonomy safe to ship.


What Highflame Does

Highflame secures AI systems across four responsibilities:

Discover — AI applications, agents, MCP servers, models, tools, and usage patterns across the organization. Understand where autonomy is running and where risk is accumulating.

Govern — AI behavior with typed Cedar policy, identity-aware controls, scoped permissions, and runtime guardrails. Shared enforcement across applications, agents, and platforms.

Monitor — AI traffic and runtime decisions through distributed traces, threat telemetry, session-aware observability, and audit-ready records.

Protect — AI systems with inline threat detection, tool and workflow enforcement, adversarial red teaming, and model supply chain validation.


Technical Architecture

Layer
Components
Role

Studio

Dashboards, Policy Management, Playground, Operator UX

Configuration and visibility

Observatory

ClickHouse, Collector Pipeline, Traces, Events

Observability and audit

Products

Guardrails, Code Agents (Overwatch), MCP Gateway, Browser Security (Sentry), Red Team, Palisade

User-facing integration surfaces

Shield

Tiered Detection: built-in (secrets, PII, injection) → ML (Raudra, toxicity, hallucination, GRU) → Cloud (DLP, ModelArmor, CheckPhish) + BYOD webhooks

Runtime threat detection

Cedar Engine

Per-service schemas (5), explain and debug modes, context projection

Policy evaluation

Core

Policy packages, Taxonomy, Identity (ZeroID), Authorization, Tenancy

Platform foundation

Detection — 25+ built-in detectors (sub-5ms), ML classifiers (10-200ms), and cloud APIs (50-500ms). Detectors declare their output keys; the projection layer normalizes them into stable semantic context for Cedar.

Policy — Cedar policies read projected context and emit permit/deny. Deny always wins. Five service-specific schemas (Guardrails, Overwatch, Sentry, MCP Gateway, Palisade) with typed actions, entities, and context attributes. The same policy framework governs MCP tool calls, IDE file operations, and Shield API evaluations.

Observability — Every evaluation produces a trace with latency breakdowns, detector results, Cedar decisions, and session state. OpenTelemetry-native. Feeds into Observatory for threat dashboards, session analysis, and command center.


Product Surfaces

Highflame is a platform, not a single-point solution. Product surfaces share the same detection, policy, and observability layers:

Product
What it protects
Integration

Guardrails

LLM prompts, tool calls, model responses, files

SDK (Python, TypeScript, Rust) or REST API

Code Agents

IDE agents — Cursor, Claude Code

VS Code / JetBrains extension

MCP Gateway

MCP server connections, tool calls, resource access

HTTP proxy or SDK

Browser Security

AI chat in browsers — ChatGPT, Gemini, Claude

Browser extension

Red Team

Agent, prompt, and workflow vulnerability testing

Studio UI or API

Palisade

ML model artifacts — safetensors, GGUF, pickle, ONNX

CI/CD (GitHub Actions, Azure DevOps)


Framework Coverage

Detections, policies, and findings map to industry security frameworks:

Framework
Coverage

OWASP LLM Top 10

Prompt injection, data leakage, tool misuse, model DoS, insecure output handling

OWASP MCP Top 10

Tool poisoning, server impersonation, cross-origin escalation, rug pull

MITRE ATLAS

Adversarial ML techniques mapped to detection signals and red team categories

NIST AI RMF

Risk measurement, governance controls, audit evidence, compliance reporting


Who This Is For

Role
Start here

Developers integrating guardrails into AI applications

Security engineers defining policies and controls

For Security Teams

Platform engineers managing multi-team rollout

Red team practitioners testing for vulnerabilities

DevOps deploying Highflame infrastructure

Deployment Guides

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