Find and control AI everywhere it runs
AI is already live across your SaaS, your cloud, your endpoints, and your code. Darkhunt discovers every one of them, tests how they break, and enforces what they're allowed to do.
What Darkhunt is used for
Six jobs, one platform, across every environment where AI runs.
AI Observability
You can't secure the agent you don't know about.
DarkHunt hunts down every model, agent, and MCP server running in your org — the ones you approved, and the ones that showed up on their own.
Sanctioned and shadow AI across SaaS, cloud, endpoint, and code
Every prompt, response, and tool call — logged, replayable, searchable
What data each agent can reach, and which of it is sensitive
Behavioral baselines that flag an agent acting unlike itself
One inventory, not one per cloud
"We found eleven agents in production. We knew about four. Nobody set out to hide them — a team spun something up for a POC, it worked, and it just never got written down anywhere."
CEO, enterprise SaaS
AI Security
We break your agents before someone else does.
Continuous automated red teaming, run against the agent as it actually runs — with its real tools, real permissions, and real data access. Not a sandbox.
Prompt injection, jailbreaks, and system prompt extraction
Multi-step tool-chain escalation across databases, APIs, and MCP servers
Data exfiltration paths the agent didn't know it had
Supply chain risk in models, agents, and MCP servers
Every finding ships with the exact steps to reproduce it
"The pentest report said 'the agent may be susceptible to injection.' May be. I couldn't do anything with that. I needed to know if it actually broke, and how."
Head of Security, financial services
AI Security · Decision Integrity
A manipulated answer looks exactly like a correct one.
Most AI security stops at "did it leak data." Darkhunt also asks whether the answer itself was bent — because an agent that quietly reports the wrong number does more damage than one that gets breached.
Test whether figures can be inflated or deflated under adversarial input
Catch suppressed alerts and omitted risks in generated summaries
Detect RAG poisoning and source attribution that doesn't hold up
Flag confident hallucination in decision-critical outputs
Trace any answer back to the exact data points that produced it
"The failure mode that keeps me up isn't leaked data. It's the agent confidently returning a wrong number to a customer, and it looking completely normal, and us finding out three weeks later."
Head of Risk, financial services
AI Governance
Set the rules. Enforce them everywhere.
Say what the agent is not allowed to do, in a sentence. Darkhunt turns it into an enforceable policy and tells you which of your obligations it just covered.
Natural-language policies — no DSL, no six-week policy project
Auto-mapped to EU AI Act, NIST AI RMF, and ISO 42001
Findings mapped to OWASP LLM Top 10 and MITRE ATLAS
Sanction approved tools and gate MCP server access
Timestamped evidence an auditor will actually accept
"Writing the policy was never the hard part. Proving it was actually enforced on every app, on the day the auditor asked — that's where every conversation fell apart."
Director of Security Compliance
AI Protection
The attack writes the guardrail.
A vulnerability report is not a control. Every finding Darkhunt produces becomes a runtime policy — deterministic, near-zero latency, enforced inline — and then we attack it again to prove it holds.
Findings convert to enforceable runtime policies automatically
Block injection and decision manipulation live, not after the fact
Mirror your RBAC — agents can't reach what the user can't
Re-tested on every prompt, model, or tool change
"We'd get a finding, then wait six weeks for someone to hand-write a rule to block it. By then the model had changed and the finding didn't even apply anymore."
Platform Security Lead
AI Assurance
Prove your AI is safe to ship.
"Is the AI safe to ship?" — with a number behind it. Coverage, blast radius, and posture per agent, that doesn't go stale the moment someone swaps a model.
Which agents are tested, monitored, and guarded — and which aren't
Risk score per agent, with blast radius and data reach
Automatic re-test when prompts, models, or tools change
Exportable, timestamped evidence for auditors and regulators
Posture over time, not a point-in-time PDF
"Leadership asks one question before launch: is it safe to ship? For traditional software I have an answer. For the agents, no.
VP Engineering
AI Inventory
You can't govern what was never written down.
Observability watches what your agents do. Inventory is the record of what exists — every model, agent, and MCP server, with an owner, a risk tier, and the data it can reach.
Every model, agent, MCP server, and dataset in one catalog
Data lineage per asset — what flows in, and whether it's PII, IP, or public
Every asset owned and accountable, sanctioned or shadow
Third-party and self-hosted models surfaced, not just the ones you bought
"The auditor asked for a list of every model we run and what data each one touches. We had a spreadsheet someone updated in Q1. That was the whole answer, and it wasn't true anymore."
GRC Lead, healthcare
A pentest is a photograph. This is a pulse.
Models change. Prompts get edited. Tools get added. Darkhunt hunts again on every change — so the posture you signed off on is still the posture you have.
Live in an afternoon.
On-prem, cloud, or hybrid — Darkhunt meets your stack where it already is. No agent rewrites, no migration project.
Unified control plane
One console for every AI agent and model across SaaS, cloud, endpoint, and code — so nothing stays invisible.
Flexible deployment
Cloud, hybrid, or self-hosted for data residency requirements. Deploy in hours, not quarters.
Seamless integrations
Connect to any agent framework, model provider, SIEM, or cloud platform with pre-built integrations.
OpenAI
Anthropic
Azure
AWS Bedrock
Gemini
Self-hosted
It's time to take control of your AI
Pick one agent. We'll run the attacks, show you what broke, and hand you the policies that stop it.