Regulatory-grade validation for AI outputs.
Driftgard validates AI outputs against policy and regulatory obligations — with versioned control packs, drift monitoring, backtesting, and audit trails built for regulated industries.
Built for compliance standards
Designed to support enterprise AI governance programs aligned to leading standards and regulatory expectations.
AI in regulated environments creates an unavoidable risk
- Policies evolve. AI behavior drifts.
- Model updates and prompt changes can silently shift outputs.
- “We didn’t mean to” does not stand up to audits or regulators.
- Most teams can’t answer: “Are we compliant today?”
Validate AI behavior against policy and obligations, track changes over time, and export audit-ready evidence tied to exact control pack and configuration snapshots.
Prove compliance in 3 steps
Validate behavior, generate evidence, and monitor for drift—without re-architecting production on day one.
Upload logs
CSV of prompt + response. No code changes required.
Get evidence
Risk score + violations + audit-ready export pack for reviews and incidents.
Monitor drift
Alert when behavior shifts after model updates or policy changes.
Outputs your compliance team can defend
- Decision: allow / warn / block
- Risk score and severity
- Clause-level violations with evidence
- Control pack + config + judge version snapshots for reproducibility
- Audit log trail of changes (who/what/when/why)
Product
Validate AI behavior with traceability, reproducibility, and oversight.
Evaluate
Validate a single prompt + response against your Control Pack. Get decision, risk score, and evidence.
Batch Evaluate
Upload datasets or chat logs and validate at scale for QA, pre-launch, and incident response.
Backtests
Replay historical data against new policy versions or judge changes to simulate impact before rollout.
Drift Monitoring
Baseline vs current windows. Detect meaningful shifts with minimum sample guards and alerts.
Audit Trail & Evidence
Immutable audit logs plus evidence exports tied to exact versions for defensibility.
Human Review (HITL)
Route high-risk or low-confidence items to review queues with reason codes and accountability.
Policy-to-Code
Upload internal policy PDFs (or YAML) and let Driftgard draft a versioned Control Pack—ready for compliance review and publishing.
Policy ingestion
Extract clauses, obligations, prohibited advice categories, required disclosures, and escalation rules.
Human validation
Draft packs are reviewed, edited, and approved with change control and audit trail.
Solutions
Verticalised for regulated environments—where proof, traceability, and oversight matter.
Responsible Gambling & Wagering
Automated monitoring for inducements and harm-signaling in accordance with AU 2026 reforms.
- Inducement / persuasion patterns
- Prohibited advice detection
- Disclosure & escalation controls
- Evidence packs for audits/incidents
Fintech & Financial Services
Validate advice boundaries, required disclosures, and privacy obligations across copilots and support assistants.
- Financial advice constraints
- Required disclaimers
- PII handling & masking
- Change impact simulation
Healthcare Support
Reduce harm risk by enforcing escalation policies and preventing diagnosis-style outputs in patient-facing systems.
- No-diagnosis boundaries
- Escalation protocols
- Sensitive content flags
- Human review workflows
Enterprise AI / Internal Copilots
Govern internal assistants (HR, legal, support, knowledge search) with policy validation and evidence trails.
- Data leakage / confidentiality policies
- HR & legal boundaries
- PII masking and retention controls
- Drift monitoring across model upgrades
Regulated teams don’t just need “guardrails.” They need defensible evidence that controls were defined, enforced, monitored, and reviewed—over time.
Pricing
Enterprise pricing is tailored by evaluation volume, retention needs, number of projects, and support scope. Below are typical starting points to help you qualify fit quickly.
- Batch evaluation
- Control pack setup (versioned)
- Evidence export pack
- Email support
- Backtests + drift monitoring
- Alerts and trend reporting
- Multiple control pack versions
- Audit logs + exports
- Human review workflows (HITL)
- Reason codes + SLA reporting
- SSO/SAML (optional)
- Dedicated support & onboarding
FAQ
Answers to common questions from compliance, risk, and AI teams.
Is Driftgard a real-time gate in front of production AI?
Driftgard supports batch validation and governance workflows that produce defensible evidence and monitoring. Real-time enforcement can be introduced later, once governance teams are comfortable and change control is established.
How do Control Packs work?
Control Packs are versioned sets of rules, thresholds, required disclosures, escalation logic, and retention settings. Every evaluation stores the exact Control Pack version and configuration snapshot to keep results reproducible over time.
What is Policy-to-Code?
Upload policy PDFs (or YAML) and Driftgard drafts a Control Pack: clauses, categories, suggested severities, and thresholds. Compliance teams review and publish the pack with full change control and audit trail.
How does drift detection work?
We compare a baseline window vs a current window and highlight meaningful changes in risk scores, violation rates, and severity distribution. Minimum sample thresholds reduce noisy alerts.
How does Human-in-the-loop (HITL) review work?
High-risk or low-confidence evaluations can be routed to a review queue. Review actions (approve/block/notes) require reason codes and are recorded in the audit log for defensibility.
What data do you store?
Storage is configurable per project. You can validate statelessly or store evaluation history for audit and drift monitoring. Retention defaults and masking options are set at the project level.
Do you support Australian data residency?
Australian-hosted options are available for regulated sectors, depending on your deployment and residency requirements. We’ll align the pilot to your constraints.
How fast can we run a pilot?
Most teams can complete an initial pilot in 2–4 weeks: policy intake, Control Pack setup, batch evaluation on sample logs, drift baseline, and an evidence export pack.
Security overview
Enterprise buyers will review security early. Driftgard is designed around tenant isolation, access controls, data minimisation, and auditability.
Multi-tenant isolation
- Org → Project binding enforced across the platform
- Partitioning prevents cross-tenant access
- Project membership required to view data
RBAC
- Role-based access (Admin/Viewer)
- Endpoint-level role checks
- Least-privilege by default
Retention controls
- Project-level retention defaults
- Stateless vs stored evaluation modes
- Config prevents callers overriding retention per request
PII masking
- Mask sensitive fields before storage (configurable)
- PII flags and evidence capture without over-retention
- Designed to support regulated review workflows
Audit logging
- Immutable audit log for key actions
- Who/what/when/why for policy and config changes
- Supports audit defensibility over time
Data hosting & residency
- Australian-hosted options available for regulated AU customers
- Exports secured via access-controlled signed delivery
- Share your requirements; we align pilot architecture accordingly
For vendor security review and compliance questionnaires, request a demo and we’ll provide an appropriate overview tailored to your deployment.
Prove your AI is compliant in 24 hours
Request a demo or a pilot audit on your existing logs. Controlled evaluation, evidence export, and drift baseline—tailored for regulated organisations.
Request demo / audit
What you’ll see in a demo
- Control Pack versioning (rules, thresholds, retention, notes)
- Evaluate + Batch runs and drilldowns
- Backtest simulation (policy/model change impact)
- Drift deltas with baseline windows
- Audit logs and evidence exports
Procurement-friendly basics
- Security overview section included
- Privacy & Terms sections included
- AU data residency options (where required)