Use Cases
AI Compliance & Governance: Meeting Enterprise Requirements
AI regulation is tightening globally, with the EU AI Act leading the way. Organizations deploying AI agents need robust governance frameworks—and context graphs provide the foundation for compliance.
Compliance Risk Alert
EU AI Act non-compliance can result in fines up to €39.82 million or 7% of global turnover. Beyond penalties, reputational damage and customer mistrust can cripple organizations.
The AI Regulatory Landscape
AI regulation has intensified dramatically. The key frameworks affecting enterprise AI deployments:
EU AI Act
The world's most comprehensive AI regulation, effective August 2024. Requires:
- • Documentation of data origins and transformations
- • Risk assessments for high-risk AI systems
- • Human oversight mechanisms
- • Extensive logging, auditing, and record-keeping
- • Transparency about AI system capabilities and limitations
GDPR
Article 22 gives individuals the right not to be subject to decisions based solely on automated processing. Requires:
- • Meaningful information about decision logic
- • Human intervention on request
- • Right to contest automated decisions
Industry-Specific Regulations
Additional requirements by sector:
- • Financial Services: SOX, Basel III, MiFID II
- • Healthcare: HIPAA, FDA guidance on AI/ML
- • Insurance: State regulations on algorithmic underwriting
Core Compliance Requirements
Across regulations, several requirements appear consistently:
Traceability
Every AI decision must be traceable—who made it, what inputs were used, what logic was applied. This is the foundation of accountability.
Explainability
Organizations must be able to explain AI decisions in terms humans can understand. "The model said so" is not sufficient.
Data Lineage
Track where data came from, how it was transformed, and how it influenced decisions. Essential for data quality assurance.
Human Oversight
Mechanisms for human review, intervention, and override of AI decisions—especially for high-risk applications.
Risk Management
Continuous monitoring for bias, errors, and drift. Processes to identify and remediate issues before they cause harm.
Record Retention
Maintain comprehensive records for regulatory inspection. Some requirements specify 7+ years of retention.
How Context Graphs Enable Compliance
Context graphs are uniquely suited for AI governance because they're designed to capture decision lineage:
| Requirement | Context Graph Solution |
|---|---|
| Traceability | Every decision trace captures who, what, when, why, how |
| Explainability | Reasoning and precedent captured in human-readable format |
| Data Lineage | Provenance metadata tracks data sources and transformations |
| Human Oversight | Actor tracking shows human involvement in decisions |
| Risk Management | Pattern detection enables proactive risk identification |
| Record Retention | Immutable, queryable storage with configurable retention |
“Governance frameworks must evolve at the same pace as technological innovation. Context graphs provide the infrastructure to make that possible.”
Building an AI Governance Framework
Essential components of enterprise AI governance:
- 1
Risk Classification
Categorize AI applications by risk level. High-risk applications (HR, lending, healthcare) need stricter controls.
- 2
Decision Capture
Implement context graph infrastructure to capture all AI decisions with full context—before you deploy agents at scale.
- 3
Monitoring & Alerting
Set up continuous monitoring for bias, drift, and anomalies. Alert on patterns that may indicate issues.
- 4
Review Processes
Establish regular review cadences. Sample decisions for human audit. Document findings and remediation.
- 5
Stakeholder Communication
Keep regulators, board, and customers informed about AI usage, controls, and outcomes.
Frequently Asked Questions
What is the EU AI Act?
The EU AI Act is the world's most comprehensive regulation of AI systems, effective August 2024. It requires organizations deploying high-risk AI to document data origins, transformations, and quality metrics, with fines up to 7% of global turnover for non-compliance.
How do context graphs help with AI compliance?
Context graphs provide built-in decision lineage, provenance tracking, and audit trails that meet regulatory requirements. Every decision trace captures the who, what, when, why, and how needed for compliance.
What are the penalties for AI non-compliance?
Under the EU AI Act, penalties can reach €39.82 million or 7% of global turnover. Beyond fines, organizations risk reputational damage, customer mistrust, and operational disruption from forced remediation.
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