End-to-end AI behavioural safety and governance system for a human-centred food companion.
This repository presents a simulated but professionally structured AI governance system based on real evaluation work conducted on ZomoBot’s behavioural safety.
To protect users, intellectual property, and organisational security:
- Internal system prompts are not disclosed
- Model architectures are not exposed
- Proprietary metrics are anonymised
- Security-sensitive controls are omitted
- Exact deployment configurations are withheld
All materials are presented for professional demonstration and portfolio purposes and reflect industry-aligned governance practices.
This repository does not represent a live production system.
This repository demonstrates how I designed and operated an end-to-end AI system governance lifecycle for a human-centred food companion serving over 50,000 users.
The framework covers:
- Behavioural safety policy design
- Evaluation Operations (EvalOps)
- Risk registration and mitigation
- Release governance and approval
- Production monitoring
- Incident management
- Root cause analysis (RCA)
- Organisational learning
- Continuous improvement
The primary focus is on preventing behavioural harm, panic induction, and authority misuse in health-adjacent AI systems.
Role: Governance Lead – AI Safety & Operations
ZomoBot is a human-centred AI companion designed to support users in food, nutrition, and lifestyle decision-making.
Because of its health-adjacent role and scale of deployment, ZomoBot operates under heightened ethical, legal, and operational obligations.
This governance framework ensures that:
- User autonomy is preserved
- Emotional and behavioural safety is protected
- Regulatory exposure is minimised
- System risks are continuously managed
Governance is treated as a core operational function, not a compliance formality.
ZomoBot operates a continuous governance loop:
Policy → EvalOps → Risk → Review → Monitoring → Incident → RCA → Postmortem → Improvement
Each stage reinforces system safety, accountability, and long-term resilience.
/01_policy → Behavioral safety policies
/02_evalops → Evaluation operations framework
/03_risk_management → Risk registers and controls
/04_release_review → Release governance
/05_monitoring → Production oversight
/06_incident → Incident case studies
/07_rca → Root cause analyses
/08_postmortem → Post-incident reviews
/09_improvement → Continuous improvement plans
/benchmarks → Safety benchmarks
/red_teaming → Adversarial testing program
/docs → Governance documentation
README.md → Repository overview
For reviewers and recruiters, the following files provide a comprehensive overview:
-
Governance Architecture →
/docs/governance_overview.md -
Behavioural Safety Policy →
/01_policy/behavioral_safety_policy.md -
Evaluation Framework →
/02_evalops/behavioral_evalops_framework.md -
Incident Case Study →
/06_incident/incident_001_tone_misuse.md -
Continuous Improvement →
/09_improvement/continuous_improvement_framework.md
This framework is supported by:
- Standardised benchmarks
- Adversarial red-teaming programs
- Risk scoring models
- Release certification gates
- Continuous monitoring systems
- Incident response protocols
All major governance decisions are documented and traceable.
The governance system aligns with:
- AI accountability principles
- Consumer protection standards
- Health-adjacent safety guidance
- Data protection regulations
Compliance considerations are embedded throughout system design and operations.
This repository reflects an integrated governance maturity level:
- Formal policies and standards
- Cross-functional oversight
- Continuous assurance mechanisms
- Institutional learning systems
Target maturity: Integrated → Optimized
Abhi Governance Lead – AI Safety & Operations
Specialization:
- AI system governance
- Behavioural safety management
- EvalOps and risk operations
- Incident and postmortem leadership
- Responsible AI implementation
Focus: Transforming ethical principles into operational systems.
This repository is published for educational, professional, and portfolio purposes.
Reuse or adaptation should preserve attribution and responsible AI principles.
For professional inquiries, collaboration, or review:
Please connect via GitHub or LinkedIn.
This repository represents a complete, end-to-end AI governance system designed for real-world, high-impact deployment environments.
It reflects how responsible AI systems should be built, evaluated, governed, and improved over time.