MITRE ATLAS™ COMPLIANT AGENT GUARDRAIL ENGINE

Secure Every AI Agent
At Machine Speed.

Discover attack surfaces, evaluate tool boundaries, enforce real-time intent guardrails, and audit autonomous LLM agent execution traces in production.

Launch Console & Assessment Read Case Study #001
ATLAS SECURITY EVALUATION ENGINE v0.2 — LIVE DEMO
STATUS: GUARDRAILS ACTIVE
99.94%
Indirect Injection Block Rate
<6ms
Guardrail Execution Latency
100%
MITRE ATLAS™ Matrix Coverage
0 Byte
Confidential Data Exfiltration
ENTERPRISE CAPABILITIES

Complete Security Posture for Autonomous AI Agents

Protect your AI infrastructure against non-deterministic LLM behavior, prompt injections, and rogue tool execution.

Agent Inventory & Posture

Automatically inventory autonomous agents, system instructions, tool bindings, and vector database access permissions across production environments.

System Boundary Evaluation

Automated non-destructive red-teaming testing prompt isolation boundaries, cross-tenant data isolation, and unauthorized parameter mutations.

Policy & Intent Guardrails

Enforce fine-grained tool authorization policies (ALLOW / BLOCK / REQUIRE_APPROVAL) before executing external database writes or wire calls.

Visual Trace Forensic Replay

Inspect step-by-step trace execution nodes showing exact user prompts, model decisions, guardrail evaluations, and tool calls.

Empirical Fix Retesting

Mathematically verify remediated vulnerabilities against hardened configuration versions (e.g., Atlas v0.1 vs v0.2) to confirm zero regression.

Executive Compliance Reports

Generate audit-ready HTML and PDF security reports detailing risk scores, threat evidence, findings, and framework compliance mappings.

1-LINE INTEGRATION

Integrate Security Guardrails in Minutes

python_agent_guard.py Python SDK v1.0
from llm_agent_security import AgentShield, PolicyEngine # Initialize enterprise guardrail shield shield = AgentShield( api_key="sec_live_9981a2...", policy_mode="MITRE_ATLAS_STRICT" ) # Inspect user prompt before passing to LLM agent evaluation = shield.inspect_prompt(user_input) if evaluation.is_blocked: print(f"[BLOCKED BY POLICY] {evaluation.threat_reason}") else: agent_response = agent.run(user_input)