DataStrict for Cybersecurity
Zero-trust, enforced as code.
A Policy Engine that turns governance from documentation into runtime enforcement. Controls are written as code, bound to identity and purpose, and proven by an immutable audit trail.
Runs in your own environment
The enforcement boundary lives inside yours.
A control layer sees every prompt, retrieval, tool call, and output - the most sensitive traffic in the company. So it has to sit inside the same trust boundary as the data it protects, not in a vendor's multi-tenant cloud. DataStrict deploys as a single-tenant container in your own private cloud, on-premises, or fully air-gapped. You hold the keys; we never receive a byte of governed traffic. Even the machine-learning detection models that score your traffic run inside that boundary - adjudication never phones home.
That deployment topology is the first security control, not an afterthought - it is the reason the enforcement below can be trusted at all. Traffic into that boundary is secured with hybrid post-quantum cryptography, so it stays sealed against a harvest-now, decrypt-later attack.
Policy as code
A signed PDF is unenforceable.
Most AI and data policy lives in a document nobody can enforce. DataStrict expresses the same intent as executable policy-as-code - reviewed, versioned, and tested like any other code - so the rule is the runtime.
When a control changes, it ships like a deploy: proposed, reviewed, simulated against real traffic, then enforced. No gap between the policy on paper and the policy in production.
policy "customer-assistant" {
applies_to = agents.support.*
region = "eu-west"
deny when output.contains(pii.ssn)
redact output.matches(pii.email, pii.phone)
allow tools = ["orders.read", "kb.search"]
deny tools = ["payments.*", "exports.*"]
escalate when adjudication.confidence < 0.8
audit = always
}Capabilities
Controls that run, evidence that holds.
Policy enforcement
Governance expressed as versioned, reviewed, testable code - and enforced deterministically across models, agents, and pipelines. No drift between what's written and what runs.
Data access control
Row, column, and field-level controls bound to identity, purpose, and jurisdiction. Zero-trust by default - least privilege is the starting position, not an upsell.
Immutable audit ledger
An append-only, hash-chained record of every decision, each entry sealed with a post-quantum signature. Evidence becomes a side effect of enforcement - queryable, tamper-evident, never reconstructed from logs after an incident.
Control Atlas
Map regulatory obligations - EU AI Act, NIST AI RMF, SOC 2, HIPAA, GDPR - to concrete, enforced controls. An auditor's requirement becomes a control that runs, not a binder.
Principles
Zero-trust, applied to AI.
Deny by default
Every action starts unauthorized. Access is granted explicitly, scoped to purpose, and revoked when the purpose ends.
Evidence, not trust
Nothing is assumed. Every decision is recorded as tamper-evident proof an auditor can verify independently.
Enforced, not advised
Controls run in the request path. A policy that only warns is documentation; a policy that blocks is security.
Govern AI like infrastructure.
Talk to our team about deploying DataStrict across your enterprise stack.