Board and executive oversight
Show how AI use aligns to risk appetite, which accountable owners are responsible, and what evidence supports effective challenge.
AI compliance operating surface
Prove AI governance at the moment risk moves.
3LS turns prompts, uploads, OAuth grants, memory, and agent tool actions into runtime evidence of which company data was allowed, warned, blocked, or reviewed before AI processing.
Regulatory direction
AI compliance now depends on operating evidence.
Can leaders challenge AI risk?
Boards and accountable executives need enough visibility to connect AI strategy, risk appetite, supplier exposure, and resilience triggers.
Can controls be proven?
Risk, security, compliance, and audit teams need evidence from the live workflow, not a reconstruction after exposure.
APRA-aligned requirements
The compliance question is no longer just "which AI tools are approved?"
APRA's April 2026 AI letter points regulated entities toward practical governance: AI literacy, lifecycle accountability, preventive controls, supplier visibility, continuous assurance, and operational resilience. Those requirements only work when the organization can see and control AI use as it happens.
Inventory and lifecycle ownership
Track approved tools, shadow use, customer-facing AI, AI-assisted delivery, agentic workflows, monitoring, change, and decommissioning.
Preventive controls
Move beyond policy direction by enforcing allow, warn, block, or review decisions before data, prompts, uploads, OAuth grants, or tool actions leave.
Supplier and concentration risk
Map model providers, SaaS platforms, embedded AI features, fourth parties, auditability, incident notice, portability, and exit assumptions.
Continuous assurance
Collect runtime evidence for model behaviour, drift, high-risk decisions, control outcomes, sensitive data handling, and independent review.
Operational resilience
Identify critical operations that rely on AI and preserve credible fallback paths when systems degrade, behave unexpectedly, or must be isolated.
Runtime evidence
Compliance evidence should be produced by the same controls that protect the workflow.
3LS records policy, control, and observability events when AI use touches sensitive data, regulated decisions, delegated authority, third-party services, or critical workflows. The result is a live evidence trail for security review, executive reporting, audit, and incident response.
Evidence stream
AI governance events
- Sensitive prompt classified before external submission
- Customer spreadsheet upload warned and acknowledged
- OAuth-connected AI app routed for review
- Agent tool action blocked outside approved workflow
- Supplier dependency recorded against critical use case
- High-risk AI decision retained for audit evidence
From the blog
Compliance Briefings Worth Reading
Coverage tied to AI regulation, runtime evidence, supplier risk, operational governance, and the control failures that show up before formal enforcement does.
Compliance10 min read
APRA's AI Letter Turns Governance Into an Operating Test
APRA's April 2026 letter makes AI governance practical: boards need AI literacy, executives need lifecycle accountability, and regulated entities need controls that prove what happened before AI changes data, resilience, suppliers, or authority.
Read article →
Thought Leadership8 min read
AI Governance Is a Runtime Control System
Organizations should stop treating AI governance as a procurement checklist. The real operating model is policy, control, and observability at the moment data or delegated authority moves into AI.
Read article →
Compliance9 min read
EU AI Act Compliance Timeline for 2025-2027
A phased view of what applies when: February 2025 prohibitions, August 2025 GPAI obligations, and 2026-2027 enforcement milestones.
Read article →
Make AI compliance visible before the review.
Map the live AI surface, define the policy decisions that matter, and collect evidence while work is still controllable.