Know what your AI can reach — before it reaches it.
Classification, datastore inventory, identity risk and shadow-AI discovery in one posture view.
From prompt to verdict in four stages.
- 01
DISCOVER
Map data sources, AI usage, agents and shadow-AI destinations across your network and logs.
- 02
CLASSIFY
Label sensitivity, data types and residency for every datastore and content stream.
- 03
SCORE
Compute posture and identity risk from exposure, dormancy, secret age, MFA status and privilege.
- 04
WATCH
Detect drift, new exposure and anomalous access with continuous monitoring and alerts.
What it does.
Data classification for content flowing into AI
Classify prompts, uploads and responses as they move toward models — PII, PHI, source code, contracts and more.
Datastore inventory with sensitivity ratings
A living inventory of every datastore, tagged by sensitivity, owner, residency and blast radius.
Posture dashboard and drift detection
Track posture score over time and get alerted when new datastores, exposures or risky patterns appear.
Identity risk scoring
Score humans, service accounts, agents and tokens on dormancy, secret age, MFA status and privilege.
DataWatcher exposure and breach monitoring
Correlate your datastore inventory with breach intelligence and misconfiguration findings.
Shadow-AI log ingestion
Upload proxy, firewall or CASB logs in CSV or JSON; GenAI destinations are aggregated by requests, users and bytes.
AI code scanning
Find AI-specific vulnerability patterns in application code before they reach production.
Model artifact scanning
Detect unsafe torch.load, pickle artifacts, Keras Lambda layers, remote model sources and missing checksums.
Supply chain risk scoring
Score model and dependency supply-chain risk with concrete mitigations and upgrade guidance.
Privacy operations
DSR intake and handling, plus ROPA records that stay aligned to your actual AI processing.
From raw log to classified inventory.
policy > "Any datastore reachable by an AI agent must be classified and scored before it is used"
datastore: "prod-customers-pg.us-east-1"
sensitivity: "HIGH — PII, payment references"
exposure: "internet-facing / no VPC endpoint"
identity_risk: { dormant_service_accounts: 2, secret_age_days: 387, mfa_gap: 0.12 }
posture_score: 0.31 / 1.0
action: "quarantine_agent_access"
audit_hash: "ed25519:4a8e…c91d" — appended to tamper-evident chain
Posture findings are mapped to the articles and control families auditors already ask for.
From blind flow to governed flow.
Unmanaged AI data flow
No inventory · no classification · no audit trail
Governed AI data flow
Inventory · classification · policy · evidence
Works together.
Runtime Guardrails
Enforce classification at the point of use — block or redact sensitive data before it reaches a model.
ExploreAgent & MCP Governance
Extend posture and identity risk scoring to autonomous agents and every MCP server they touch.
ExploreCompliance & Evidence
Turn every classification and posture change into signed, auditor-ready evidence.
Explore