Secure AI agents, tools, and decisions with Intertrace
The runtime layer for enterprise AI security
When an agent acts, three things happen at once: a prompt, a tool, a decision. Intertrace inspects all three while the call is still open, holds what should not run, and keeps a record a person can take to review. That is the difference between hoping the model behaves and operating a runtime you can defend.
Your agents have permissions.
But do they have authority?
That gap becomes dangerous as agents gain more autonomy.
An agent with access to your systems does not need malicious intent to cause damage. A misunderstood instruction, compromised tool, prompt injection, excessive permission, unexpected reasoning path, or incorrect decision can be enough.
And once the action has happened, traditional logs often leave security teams reconstructing the story across dozens of disconnected systems.
Intertrace is built to stop that.
Intertrace places deterministic policy, compact intent routing, and managed AI safety checks between your applications and LLM providers. Runtime verification and Guardian detection run on the Intertrace-hosted edge, with stage-level latency telemetry you can verify in your own environment.
+The platform
Three layers. One integration.
The privileged moment is the API call. Intertrace sits where the traffic already flows and reasons at the same layer as the model.
AI Security Gateway
Real-time classification
Every API call flows through Intertrace classifiers for injection, jailbreaks, PII, and anomalies — with tool-call policy and versioned policy packs feeding the same control plane your security team uses.
- Neural injection & jailbreak detection
- PII detection & redaction
- Tool-call policy + policy packs
Runtime Verification
Agent behavior monitoring
Runtime watches behavior end-to-end: intent alignment, chain-of-thought, and tool usage. Correlate traffic with run IDs, inspect spans and gateway hops, and fail closed when verification demands it.
- Chain-of-thought inspection
- Intent-alignment scoring
- Autonomous guardrail enforcement
Guardian detection & response
Review · recommend · approve
Guardian continuously reviews AI traffic and estate risk, then proposes prioritized actions for Work. Ask Intertrace is the interactive analyst chat — separate from Guardian. AI proposes; humans approve.
- Scheduled and event-driven risk reviews
- Ask Intertrace with cited evidence
- Approval-gated remediation
+Gateway
Decisions at gateway latency — not post hoc.
Every prompt and response is classified inline. Block, redact, or allow with a structured event on every hop.
+Runtime
Your AI passed every test. Then it went rogue in production.
Runtime verification proves what the agent is doing now — not what it once scored on a benchmark.
+Guardian
AI proposes. Humans approve.
Guardian reviews traffic and estate risk, then queues prioritized actions for Work. Ask Intertrace is the analyst chat — separate from Guardian.
+Live demo
Try it. Right now.
Type any prompt. Watch Intertrace classify it in real time.
Type a prompt or pick a scenario. Responses use Intertrace Unified Detection Engine (IUDE) — the same Simulation Lab stack as the full page.
+Protects the stack you already run
Models. Builders. Apps.
Intertrace sits in the call path — whether the client is a foundation model, an agent framework, or an application that already talks to tools. Same runtime. Same hold. Same record.
+Models · LLM providers and hosted inference
+Agent builders · Frameworks and orchestration
+AI applications · Products that call models and tools
- OpenAI
- Anthropic
- Google Gemini
- AWS Bedrock
- Azure OpenAI
- Mistral
- Meta Llama
- Hugging Face
- OpenRouter
- Perplexity
- Ollama
- DeepSeek
- Replicate
- Cloudflare
- Groq
- Baseten
- LangChain
- CrewAI
- Vercel AI SDK
- Pydantic AI
- Haystack
- n8n
- Make
- Zapier
- OpenAI Agents
- LlamaIndex
- AutoGen
- LiteLLM
- Mastra
- Semantic Kernel
- ChatGPT
- Claude
- Cursor
- GitHub Copilot
- Microsoft Copilot
- Gemini
- Notion
- Slack
- Linear
- Intercom
- Salesforce
- Zendesk
- Figma
- Microsoft Teams
- Palantir
- Databricks
+Why AI protecting AI
The future is agentic. So are the attacks.
AI runs on trust, but agents and tools expose the weakest links. Without inspection at input, execution, and output, trust is a story you tell after the call. Intertrace is built for the moment the agent still has work to do.
Attacks are language, not CVEs
LLM abuse doesn't ship as a versioned vulnerability — it's new phrasing, new tool chains, new social games every week. The defense has to be in the same class of capability as the model.
Control sits in the request path
The privileged moment is the API call: prompt in, model work, text or tools out. Offline review files incidents after users already saw a bad answer. Decisions belong at gateway latency.
Policy is scope, not a blocklist
Enterprise policy is who the agent is for, what data it may touch, and which tools it may use — not fifty thousand disallowed substrings. Enforcing that takes reading meaning and intent.
+Capabilities
Gateway through evidence.
Classify traffic, enforce tool policy, verify runtime behavior, run Red Team and assessments, and ship evidence your team can stand behind.
+Gateway & policy
Prompt injection defense
Novel attacks surfaced on first contact.
PII detection & redaction
Broad entity coverage; redact before forward.
Tool execution policy
Org and per-asset rules; merged policy packs.
+Runtime verification
Intent alignment
Surface drift when behavior leaves declared intent.
Reasoning verification
Inspect chain-of-thought, not only final text.
+Behavioral intelligence
Behavioral baselines
Per-agent profiles; flag meaningful change.
Risk scoring
Posture signals mapped to common frameworks.
+Security testing & evidence
Red Team scenarios
Curated probes, presets, trend vs prior run.
Probe assessments
Queued profiles against your gateway; summaries and artifacts.
Managed agents
Intel, posture, monitoring, incident workflows.
Reports & exports
PDF and structured exports for audit.
+Use cases
Built for every team shipping AI.
From regulated enterprises to fast-moving product teams — Intertrace adapts to your market, your agents, and your risk. One runtime control plane, every scenario.
Financial services
Customer copilots & fraud agents
Block prompt injection that probes for account data, enforce least-privilege access to ledgers and payments, and hand auditors a defensible record of every automated decision.
Healthcare & life sciences
Clinical & patient assistants
Redact PHI in real time, stop unsafe medical guidance before it ships, and keep HIPAA-aligned evidence of what each agent saw, said, and did.
Customer support
Support & success agents
Shut down jailbreaks and refund-abuse exploits, keep agents on-policy, and verify intent before a tool call issues a credit or edits an order.
Software & engineering
Coding copilots & dev agents
Catch secret and credential exfiltration, block malicious command or tool execution, and watch for autonomy drift in CI and background agents.
E-commerce & retail
Shopping & merchandising agents
Prevent price and discount manipulation, PII leakage, and prompt-injected checkout abuse — at Black-Friday scale, at gateway latency.
Government & public sector
Citizen-service & analyst agents
Enforce strict data-handling policy and generate audit trails mapped to NIST AI RMF and OWASP LLM.
Legal & professional services
Research & drafting assistants
Keep privileged material in-bounds, prevent confidential leakage across matters, and log chain-of-thought so reviewers can defend every output.
Insurance
Claims & underwriting agents
Verify automated decisions against policy, flag reasoning that drifts from declared intent, and document every action for regulators and appeals.
Framework tags (HIPAA, SOC 2 / PCI, NIST AI RMF) are security-control mappings — not certifications or attestations.
Don't see your scenario? Intertrace is model- and framework-agnostic — if your agents touch prompts, tools, or data, we can secure them.
Map your use case+Agent attack graph
Follow the attack path—not a wall of logs.
What it is. The agent attack graph is a live map of your AI system: nodes (agents, tools, models, and data) and edges (how control and data moved). The attack path is the highlighted route that shows how abuse propagates from first touch to impact—one coherent chain instead of a pile of disconnected log lines.
What it helps with. Faster triage, clearer blast radius, and a defensible story for security, legal, and leadership—what connected to what, in order, with the framework tags your auditors and SOC expect. The Simulation Lab uses the same graph-on-top, story-below layout with multiple mock scenarios and full readouts.
Poisoned PDF → RAG → support & billing agents → 1.2M PII (Customers DB)
Sample attack path
Poisoned PDF → RAG → agents → sensitive customer data
Bad content entered the RAG context, then steered support and billing agents toward the customer database. The red path is that chain in one view—hops a wall of log lines usually won’t connect.
What it helps with: Helps you brief IR, legal, and leadership on what actually ran, in order—without rebuilding the story from tickets and raw telemetry.
Open the Simulation Lab for four mock scenarios, full risk readouts, and outcome detail.
- Nodes
Entities in the run: agents, tools, models, data stores.
- Edges
Flow between them—the attack path is the highlighted red chain.
- Findings
OWASP / MITRE-style tags on the risky surfaces.
Intertrace fuses gateway telemetry, agent behavior, and data touch so you get one defensible view—not tickets plus raw logs. The canvas above is the real product graph component, not a screenshot.
+Integration
One integration. Full protection.
Change one URL. Traffic hits the Intertrace-hosted edge and starts classifying immediately.
# One line. Full protection.
client = OpenAI(
base_url="https://gateway.intertrace.ai/v1"
)Your AI is only as secure
as what watches it.
+ The AI watching your AI
Experience runtime agent security
Book a 30-minute risk assessment — or open the lab on a live path.