Posture assessment
Provenant Scan
Assesses AI posture, validates policy, detects threats, and produces evidence for build-time and architecture review gates.
Posture score
Policy findings
Threat signals
Provenant · build-time assurance for AI agents
Your agent trusts every tool description it reads.
A tool description is not documentation — it is input the model ingests as instruction. Provenant red-teams your MCP servers, tools, and agent configurations before they ship, then signs evidence of exactly what it checked.
Attack classes
6 covered
Controls
10 mapped
Frameworks
5 crosswalked
Evidence
signed
A live view of the Provenant control plane: trust index, assets and agents under control, policy gates cleared, and the flow from AI systems through the control plane into evidence, policy, and audit.
AI security command center
Trust index
in band
assets
agents
gates
Control plane
streaming
Illustrative interface — not live customer data
The product
One control plane for the agents you already run
Four modules over a single evidence chain. Register what exists, scan it for the attack classes that target agents, enforce policy before release, and keep watching after it.
Continuous monitoring
Provenant Monitor
Monitors runtime behavior, drift, agent actions, model exposure, tool usage, and control health across production AI systems.
Runtime telemetry
Drift alerts
Control status
Policy and reporting
Provenant Govern
Maps controls to frameworks, automates evidence collection, manages exceptions, and generates audit-ready reporting.
Policy packs
Evidence trails
Audit reports
AI asset system of record
Provenant Registry
Maintains the inventory of AI assets, LLMs, agents, prompts, MCP servers, APIs, datasets, and tools.
Asset inventory
Ownership map
Risk context
What it reasons about
Security, governance, and research for AI systems that matter
Provenant is built on research into how agent systems actually fail. These are the surfaces it covers.
AI security
Security engineering for LLM applications, agents, model pipelines, cloud services, and runtime control planes.
Agent securityPrompt securityLLM securityMCP securityAPI securityModel securityIdentity and secretsSupply chain securityRuntime monitoring
AI governance
Evidence-driven governance for AI assets, model registries, policy workflows, audits, and regulatory alignment.
AI asset inventoryModel registryAgent registryPolicy engineRisk frameworksCompliance automationEvidence collectionAudit reportingNIST AI RMF
AI research
Threat research, standards analysis, training material, and applied field work for enterprise AI risk teams.
Threat researchWhite papersStandards mappingTechnical trainingIndustry analysisControl patternsAdversarial testingAgent failure modes
Measured, not asserted
Every detector is benchmarked before it ships
Detection rate and false-positive rate are measured against a versioned corpus of labeled attack and benign fixtures. The benchmark runs in continuous integration and fails the build when either number regresses, so these figures are reproducible rather than claimed.
Corpus 2026.09.1 · 38 fixtures · verified on every change to the engine
100%
Detection rate
0%
False positives
14
Detectors mapped to controls
5
Frameworks covered
Services
Help getting it running
Some teams want the platform and nothing else. Others want help standing up the controls behind it. The services practice exists to support adoption, and every engagement feeds the control library back into the product.
Advisory
Board, CISO, CTO, and risk leadership support for AI governance strategy, operating model design, and standards alignment.
- AI risk management operating model
- NIST AI RMF and ISO 42001 readiness
- CISO and architecture briefings
- Governance roadmap and control ownership
Technical
Hands-on security engineering for AI applications, agents, cloud control planes, runtime monitoring, and policy enforcement.
- LLM and agent threat modeling
- Prompt and MCP security reviews
- Red-team harnesses and validation gates
- Runtime telemetry and evidence pipelines
Training
Practitioner-grade training for security, platform, product, risk, and architecture teams operating enterprise AI systems.
- Executive AI risk workshops
- Agent and LLM security labs
- Cloud AI security training
- Policy-as-code and audit evidence labs
Built around the frameworks enterprise AI teams are already mapping
NIST AI RMFISO 42001OWASP Top 10 for LLMSOC 2PCIHIPAAGDPRMCP securityMITRE ATLAS
Find out what your agents are actually exposed to.
Send us your MCP server and agent configurations. We run Provenant against them and hand back findings, control coverage, and signed evidence. Two weeks, no cost, no commitment.