Driftgard
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Built for regulated industries & audit defensibility

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.

Designed for regulated audits — equally effective for teams shipping AI safely at scale.

Request a demo See how it works View product snapshots
Designed for teams who must answer: “Are we compliant today?” and “Can we prove controls existed at the time?”

Built for compliance standards

Designed to support enterprise AI governance programs aligned to leading standards and regulatory expectations.

ISO/IEC 42001 NIST AI RMF AUSTRAC AML/CTF SOC 2 readiness
Design partner program
Leading Gambling Operator
Global Bank
Healthcare Provider
Enterprise Support Team
Gov/Reg Advisory

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?”
Driftgard is a validation + regulatory defense platform

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.

1

Upload logs

CSV of prompt + response. No code changes required.

2

Get evidence

Risk score + violations + audit-ready export pack for reviews and incidents.

3

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)
{ "decision": "warn", "risk_score": 0.72, "violations": [ { "rule_id": "rg-inducement", "severity": "high", "evidence": "Message implies bonus/inducement." } ], "control_pack_version": "rg-au-v3", "judge_version": "judge-v2", "timestamp": "2026-03-04T00:00:00Z" }

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.

{"decision":"block","risk_score":0.91,"violations":[{"rule":"rg-prohibited-advice","severity":"high"}]}

Batch Evaluate

Upload datasets or chat logs and validate at scale for QA, pre-launch, and incident response.

{"job":"batch_202603","rows":10000,"blocked_pct":3.4,"warn_pct":11.2}

Backtests

Replay historical data against new policy versions or judge changes to simulate impact before rollout.

{"from":"cp_v3","to":"cp_v4","block_delta":"+1.9%","top_clause":"rg-inducement"}

Drift Monitoring

Baseline vs current windows. Detect meaningful shifts with minimum sample guards and alerts.

{"baseline":"30d","current":"7d","risk_delta":"+0.08","min_samples":500}

Audit Trail & Evidence

Immutable audit logs plus evidence exports tied to exact versions for defensibility.

{"export":"evidence_pack","policy":"cp_v4","signed_url":"true","retention_days":90}

Human Review (HITL)

Route high-risk or low-confidence items to review queues with reason codes and accountability.

{"queue":"high_sev","reviewed_within_hours":12,"override_reason_required":true}

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.

Upload CSV logs → Evaluate → Risk score + evidence pack ↘ Backtest new policy → compliance delta ↘ Monitor drift → alerts / weekly reports

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
Why this matters

Regulated teams don’t just need “guardrails.” They need defensible evidence that controls were defined, enforced, monitored, and reviewed—over time.

Also used for general AI governance

Same validation engine. Different risk tolerance.

Where teams use Driftgard

  • Internal copilots (HR, legal, support)
  • B2B SaaS teams shipping AI features
  • Startups needing policy testing + drift monitoring before launch
Same validation engine. Different risk tolerance.

Use the same control packs, backtests, drift monitoring, and evidence exports—tuned to your organisation’s risk thresholds and deployment stage.

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.

Pilot2–4 weeks
AUD $2,500
Controlled audit on your existing logs—fast proof, low friction.
  • Batch evaluation
  • Control pack setup (versioned)
  • Evidence export pack
  • Email support
Start pilot
ComplianceMost popular
From AUD $7,500/mo
Ongoing governance: drift monitoring, backtests, and oversight reporting.
  • Backtests + drift monitoring
  • Alerts and trend reporting
  • Multiple control pack versions
  • Audit logs + exports
Request demo
EnterpriseCustom
Contact us
Advanced oversight and enterprise controls.
  • Human review workflows (HITL)
  • Reason codes + SLA reporting
  • SSO/SAML (optional)
  • Dedicated support & onboarding
Talk to sales

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
Security questionnaires

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

Tip: If you don’t want to upload logs yet, request a demo and we’ll walk through an anonymised sample aligned to your policy structure.

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)