AI Governance in each SDLC Phase

For a long time, AI was a mystery, and 85% of early projects failed because legal or fairness problems were found too late. The real solution is not one final check, but making the entire Software Development Life Cycle (SDLC) an ethical journey. Instead of relying on simple scores, the Planning phase now requires measurable fairness results, such as 95% equalized odds (the model must achieve nearly the same success and error rates for all groups). Integrating these rules into the Design, Coding, and Testing steps makes it easy for teams to follow the law, effectively mitigating bias and significantly reducing expensive rework down the line. This approach to incorporating AI Governance at every step is crucial for ensuring our AI is reliable, compliant, and ready for inspection.

AI Governance in each SDLC Phase

1. Planning Phase: Defining Scope and Risk

In the planning phase, governance focuses on establishing the project’s ethical and regulatory boundaries before any technical work commences. This is where the overall risk is assessed and classified.

Key Governance Actions

• Initial Risk Assessment: Conduct an ethical and regulatory assessment to classify the potential societal impact of the AI system.
• Define Purpose: Clearly define the intended use case and verify that the objective aligns with the organization’s ethical guidelines and legal requirements.
• Resource Allocation: Allocate necessary resources for governance activities, including the time required for compliance officers and ethics committee reviews.

2. Requirements Analysis Phase: Establishing Boundaries and Metrics

This phase translates high-level risks into concrete, measurable technical requirements for fairness, transparency, and data use.

Key Governance Actions

• Metrics Definition: Establish target fairness metrics (e.g., equal opportunity, disparate impact) and minimum performance thresholds.
• Explainability Mandate: Define the required level of Explainability (XAI) necessary to comply with regulations and ensure user trust.
• Data Scoping: Define and vet data sources, ensuring legal rights to use the data and identifying necessary privacy controls upfront.

3. Design & Architecture Phase: Designing for Compliance

The design phase integrates governance requirements into the technical blueprint, ensuring the system is built securely and in a manner that is accountable.

Key Governance Actions

• Privacy by Design: Architect the data pipeline to enforce data privacy measures (e.g., anonymization, pseudonymization).
• Security Design: Implement defensive architecture to protect the model and data against adversarial attacks (e.g., model poisoning).
• Model Card Framework: Design the structure and content requirements for the official Model Card documentation, making it a required design output.

4. Development & Coding Phase: Building Securely and Accountably

During the building and training process, governance focuses on controlling inputs and ensuring reproducibility of the model output.

Key Governance Actions

• Bias Mitigation: Actively implement and apply bias mitigation techniques during model training and iteration.
• Version Control: Mandate strict version control for the code, training data, and resulting model artifacts to ensure full traceability.
• Secure Coding: Apply security practices focused on the ML codebase to prevent vulnerabilities and secure secret management.

5. Testing & Validation Phase: Verifying Ethics and Robustness

This is the critical quality gate where governance validates the system against all predefined ethical, regulatory, and performance requirements.

Key Governance Actions

• Compliance Testing: Conduct thorough testing to ensure the system meets all legal, regulatory, and internal governance requirements.
• Adversarial Resilience: Test the model’s robustness against various adversarial attacks and boundary violations.
• Fairness Validation: Final validation against all target fairness metrics and sign-off on the completed Model Card documentation.

6. Deployment Phase: Controlled Release and Handover

Governance ensures that the transition to production is controlled, monitored, and supported by clear operational procedures.

Key Governance Actions

• Governed Release: Implement a formal, phased rollout plan with sign-offs from legal and risk teams before full production release.
• Incident Protocol: Ensure clear runbooks and an incident response protocol are established to handle immediate ethical or performance failures.
• Operational Handoff: Formally transfer responsibility to the Operations team, ensuring they have the tools and training to monitor governance controls.

7. Maintenance & Operations Phase: Sustaining Governance Post-Deployment

In the maintenance phase, governance ensures continuous risk assessment, model retraining, and performance audits. Regular reviews help identify issues such as model drift, bias reintroduction, or compliance gaps, ensuring the AI system remains trustworthy and reliable over time.

Key Governance Actions

• Continuous Monitoring: Implement real-time systems to track the model’s ethical and performance metrics against production data.
• Scheduled Audits: Schedule periodic internal and external audits and compliance checks to verify adherence to regulatory standards.
• Model Retraining Governance: Define clear triggers, data vetting processes, and governance sign-offs for model retraining to ensure new versions do not introduce bias or degrade performance.

Certified AI Governance Specialist (CAIGS) Training with InfosecTrain

AI governance is not a one-time task but an ongoing commitment embedded throughout the entire SDLC, ensuring systems are ethical, explainable, and compliant. Integrating these principles from planning to maintenance builds long-term trust and accountability into AI systems. The InfosecTrain Certified AI Governance Specialist (CAIGS) Training provides the necessary comprehensive knowledge to govern AI responsibly and at scale. The program covers the entire AI lifecycle, blending theory, regulations, risk management, and practical auditing skills. Equipping professionals to operationalize these governance programs ensures fairness, transparency, and compliance, future-proofing both careers and AI initiatives.

TRAINING CALENDAR of Upcoming Batches For

Start Date	End Date	Start - End Time	Batch Type	Training Mode	Batch Status
12-Jan-2026	16-Feb-2026	20:00 - 22:00 IST	Weekday	Online	[ Open ]	Enroll Now