Security research
Threat research, standards analysis, and adversarial testing that turn new attack classes into detectors with a benchmark behind them.
Agent failure modesPrompt and MCP attack surfaceStandards mapping
About
We build the assurance layer for AI agents.
Provenant red-teams your MCP servers, tools, and agent configurations before they ship, then signs evidence of exactly what it checked. It comes out of research into how agent systems actually fail — not in the model, but in the seams around it.
The seams, not the surface
Most AI programs secure the prompt or the model in isolation while the real exposure sits in the seams: the identity a tool call runs as, the data boundary a retriever crosses, the MCP metadata an agent trusts, the pipeline that ships a model without a gate. Research starts there because that is where incidents start.
Research that has to ship
A finding is not finished until it is operational: a control with a named owner, a policy a pipeline can enforce, or a detector inside Provenant. Every attack class we publish ships as a detector measured against a labeled corpus, and the benchmark fails the build if detection or false-positive rate regresses.
Early, and saying so
Provenant is early. The engine runs, the detectors are benchmarked, and the control plane is deployed — self-serve onboarding is not. We publish where the product actually is rather than let you find out after you have spent time on it, and we would rather release what we learn than hold it back as a sales asset.
How we work
What we hold to
Five commitments that decide what we will and will not put our name to.
Rigor before reassurance
We would rather tell you a control is unproven than sign off on one we cannot evidence. Confidence is an output of testing, not a tone of voice.
Evidence over assertion
A control is real when it can be evaluated, traced to an owner, and exported for audit. The risk paper an executive reads and the policy a pipeline enforces describe the same control, or the control does not exist.
Independent by default
No reseller margin, no partner tier, no referral fee shapes a recommendation. The advice follows the risk in your estate.
Capability, not dependency
The work succeeds when your team can run the control without us in the room. We build the muscle and hand over the evidence.
Security that widens access
Assurance should let an organization adopt AI further and faster than it safely could before, not become the reason it stalls.
The work
What the practice covers
Four areas sit behind the product. Research finds the failure modes, engineering turns them into controls, governance maps them to the frameworks you already report against, and training makes them operable by the people who own them.
Security engineering
Identity, secrets, data boundaries, runtime monitoring, and the gates that sit in front of a release.
Workload identityRuntime containmentPolicy as code
Governance and audit
Control libraries mapped to the frameworks you already report against, with evidence pipelines that survive an audit.
NIST AI RMF and ISO 42001Evidence automationException handling
Training and enablement
Curriculum built on the same labs and incidents the detectors come from, so the people who own a control can operate it.
Cloud security labsAgentic AI securityExecutive workshops
A product company, with the practice that proves it works.
Provenant is what we build: registry, scan, governance, and monitoring over one evidence chain. The services practice exists to get it adopted and to feed real estates back into the control library — not the other way around.