The Fenergo AI Governance Whitepaper presents Fenergo’s structured approach to ensuring trustworthy, transparent, and compliant use of AI across its SaaS platform.
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Executive Summary:
Fenergo emphasizes the importance of trustworthy AI built on transparency, accountability, and ethical safeguards. Its SaaS AI Control Framework translates these principles into operational requirements that help clients deploy AI capabilities confidently -
Regulatory Landscape:
The framework aligns with global standards including:-
EU AI Act – covering system classification, conformity assessments, and post-market monitoring.
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U.S. NIST AI Risk Management Framework and Executive Orders on trustworthy AI.
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UK’s principles-based framework emphasizing transparency, fairness, and accountability.
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Singapore’s FEAT principles and related MAS, PDPC, and IMDA guidance.
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Hong Kong’s OGCIO and PCPD guidelines on ethical AI and privacy compliance
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SaaS AI Control Framework:
Over 30 controls address regulatory concerns such as explainability, bias and fairness, auditability, privacy, human oversight, and accountability across the AI lifecycle -
Core Framework Components:
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Human Oversight: Agents operate within predefined limits, with full visibility and intervention capability via the Command Centre.
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Auditability: Every AI action is logged with traceability for regulatory and operational review.
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Accountability: All AI use is opt-in, allowing clients full configuration control.
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Explainability: The Agent Rationale function provides structured explanations for AI decisions.
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Fairness & Bias Mitigation: Systems use neutral models built on AWS Bedrock, not trained on client data.
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Privacy Compliance: Ensures strict data segregation and localization per client AWS region
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Security by Design:
Fenergo secures data with encryption at rest and in transit, enforces strict access controls, and aligns with ISO/IEC 27001 and SOC 2 standards. Real-time monitoring and incident response strengthen resilience and integrity -
Continuous Improvement & Client Empowerment:
Built on AWS Bedrock, the system enables continuous updates, stability, and client customization of AI logic without disruption -
Trusted LLM Integration (AWS Bedrock):
AI runs exclusively on AWS Bedrock, ensuring no training or fine-tuning on client data, full traceability, guardrails for bias prevention, and human oversight for all decisions -
Client Outcomes:
The framework enhances regulatory alignment, operational oversight, privacy assurance, and strategic flexibility, giving clients confidence to adopt AI responsibly while maintaining full control -
Conclusion:
The framework embeds responsible AI principles—transparency, security, and accountability—into every stage of design and deployment, enabling clients to build trust, unlock value, and future-proof their operations
For more information, access the attached whitepaper available for download.