AI Agent Compliance

Autonomous Agents Need Autonomous Compliance

When AI agents make security decisions at machine speed, compliance can't wait for a human to fill in a spreadsheet. TIA builds audit, explainability, and governance into the agent architecture — not as an afterthought.

Core Principles

How TIA approaches compliance

☷

Every Decision Has a Trail

Every agent action is logged with context: what triggered it, what data it used, what confidence level it had, and what happened next. Decisions form chains, not isolated events.

Decision Graph
⚖

Confidence Is Explicit

Agents don't just act — they declare how certain they are. Every edge in the decision graph carries a confidence score (0.0 to 1.0). Regulators see not just what happened, but how sure the agent was.

Confidence Tiers
⚙

Sovereign by Default

Customer data never leaves customer infrastructure. TIA agents run on-premise. No cloud dependency. No vendor lock-in. Your data, your servers, your jurisdiction.

Data Sovereignty
↺

Identity Persists, Decisions Don't Disappear

TIA agents maintain persistent identity across model swaps and restarts. Their decision history survives infrastructure changes. You can audit an agent's full lifecycle — not just its last session.

Persistent Memory
☍

Staleness Detection

Decisions made on outdated context are flagged automatically. If an agent acts on stale threat intelligence or expired facts, the audit trail shows it. No silent failures.

Context Integrity
◤

Execution Flows, Not Isolated Events

Agent actions are grouped into execution flows — detection, analysis, response, validation. Regulators see the reasoning chain, not a flat log of disconnected events.

Flow Tracking
Architecture

Compliance infrastructure

Decision Graph

NODES Agents, decisions, facts, incidents, actions, regulations LIVE
EDGES triggered_by, based_on, resulted_in, escalated_to — with confidence scores LIVE
EXECUTION FLOWS Ordered reasoning chains with outcome tracking and duration LIVE
TEMPORAL VALIDITY Facts and edges carry valid_from / valid_to windows — prevents stale-state decisions LIVE
COMPLIANCE EXPORT Audit-ready reports from decision graph data BUILDING
CUSTOMER DASHBOARD Read-only compliance view for CISOs and regulators PLANNED
Regulatory Landscape

Preparing for what's coming

EU AI Act

HIGH-RISK AI SYSTEMS — 2026
  • Risk management system (Article 9)
  • Technical documentation (Article 11)
  • Record-keeping and logging (Article 12)
  • Human oversight provisions (Article 14)
  • Accuracy, robustness, cybersecurity (Article 15)
  • Incident reporting (Article 62)

NIS2 Directive

CRITICAL INFRASTRUCTURE — ACTIVE
  • Incident response and reporting
  • Supply chain security
  • Vulnerability disclosure
  • Risk management measures
  • Business continuity planning

SOC 2 / ISO 27001

INDUSTRY STANDARD — ONGOING
  • Security monitoring and alerting
  • Access control and authentication
  • Change management audit trail
  • Incident management procedures
  • Continuous risk assessment
Production Evidence

Not theory — production

AGENTS IN PRODUCTION 35 autonomous agents, 24+ days continuous operation
THREATS BLOCKED 563 attacks blocked, 0 breaches
CERT REPORTS 2 cases confirmed by national CERT (NUKIB)
OPERATING COST $405/month total operating cost — $11.57 per agent per month
MODEL SWAPS 3 model swaps survived — agent identity and decision history persisted
DETECTION TIME 21-second average detection — autonomous response, no human in the loop

Let's talk compliance for autonomous agents

Building compliance infrastructure for AI security operations is a new problem. We'd rather solve it with partners than alone.

Get in touch