Hands-On Certified AI Governance Engineering with Python

Use usage telemetry as the visibility layer for AI governance to track who uses AI, where and when, what tools are used, and how often, turning activity into governance signals.

Explore model usage analytics to measure call counts, utilization, and top applications and use cases, guiding governance decisions on cost, risk, reliability, and value.

Track token events and allocate AI costs by team, use case, model, and vendor to reveal value, waste, and opportunities for cost governance.

Establish clear accountability by defining business, technical, approver, reviewer, and vendor manager roles with evidence-based decisions and ongoing monitoring. Maintain auditable records to ensure governance, risk management, and vendor accountability.

Explore the multidimensional AI risk landscape, including privacy, security, bias, policy, operational, legal, financial, and reputational risks, and learn how governance across people, process, technology, and oversight mitigates them.

Frame risk factors as signals guiding governance for AI systems, analyzing data sensitivity, user impact, autonomy, model exposure, model capability, and regulatory scope to determine appropriate controls and oversight.

Assess inherent risk before safeguards, apply layered controls, and determine residual risk. Formalize risk acceptance with clear criteria, ownership, and evidence for governance across the ai lifecycle.

Develop and implement an AI risk scoring and remediation engine using Python through a hands-on governance engineering project.

Balance accuracy, precision, recall, latency, and Svetlana's score to assess production-ready model quality. Define tasks, use representative data, run predictions, and monitor metrics to protect the user experience.

Learn to evaluate generative ai with dimensions of relevance, faithfulness, groundedness, answer quality, and safety, using rubrics, grounded sources, and iterative, action-oriented evaluation.

Monitor data drift, concept drift, performance degradation, and model degradation signals to keep AI models reliable as real-world conditions evolve. Use baselines, thresholds, signals to guide investigation and retraining decisions.

Turn evaluation results into data-driven production decisions using evaluation thresholds, quality gates, release criteria, and rollback plans with evidence and a safe fallback.

Treat prompts as governed assets with clear ownership, purpose, versioning, testing, approval, and retirement. The lifecycle ensures traceable, testable, and auditable behavior across use cases.

Capture structured logs of every interaction, including timestamp, request identifier, prompt version, model name and parameters, input and response summaries, sources, and outcomes, to enable traceability, reproducibility, and governance.

Explore ELELM evaluation as a complete system by combining repeatable test sets, rubrics, automated judges, human review, and adversarial prompts to measure usefulness, safety, consistency, and risk.

Evaluate how prompt, model, and configuration changes ripple through EYI systems, affecting tone, safety, and user experience. Use a repeatable, evidence-based change impact workflow with regression testing and rollout decisions.

Master hands-on AI governance engineering with Python by building a prompt and response governance lab.

Define agent observability by capturing execution plans, tool calls, retries, errors, and outcomes to reveal the agent's reasoning and actions for governance, accountability, and safety.

Identify high-risk agent failure modes and implement governance practices to keep unauthorized actions, unsafe tools, runaway loops, and poor escalation visible, bounded, and escalated.

Explore how human-in-the-loop design embeds review queues, approval gates, override rules, and escalation paths into AI governance, ensuring accountable, evidence-based decision-making.

Measure human oversight by tracking review rate, approval time, escalation rate, override rate, and reviewer outcomes to ensure evidence-based governance.

Engage in a hands-on project to build an ai agent activity and a human oversight monitor, applying ai governance engineering practices with python.

Learn to manage RAG governance by ensuring content freshness, provenance, relevance, and permission-aware retrieval; implement robust freshness metrics, citation trails, and access controls to prevent data leakage.

Identify data types, classify sensitive information, and apply controls including masking, encryption, least privilege, and approvals to prevent, detect, and respond to data exfiltration across prompts, logs, and exports.

Enforce access controls, document permissions, and source filtering to ensure identity-aware retrieval stays within approved domains, applying least privilege and delivering restricted, compliant answers.

Engage in a hands-on project to build a retrieval-augmented governance dashboard for sensitive data. Learn practical techniques for AI governance engineering with Python to manage risk and data privacy.

Translate broad regulations into concrete, auditable AI controls by turning obligations into control objectives, defining control activities, and documenting evidence to prove compliance.

explore the full policy life cycle from draft to audit and learn how scope, purpose, audience, responsibilities, versioning, publishing, updates, and retirement keep policies current and auditable.

Learn to version rules and policies with an auditable, controlled process that tracks effective dates, approvals, impacted systems, and historical applicability.

Frame evidence management as the proof layer of AYEY governance by collecting artifacts that show design, testing, approvals, and exception handling.

Build a compliance mapping and policy versioning engine through a hands-on project, applying practical techniques for AI governance engineering with Python.

Explore how to conduct an AI impact assessment as a decision record that defines intended use, identifies affected users, surfaces harms and limitations, details controls, and tracks residual risk.

Exception management is a governance lifecycle that records risk acceptance, compensating controls, expiry, and accountability from request to closure, with defined scope, owner, and evidence for re-approval.

Develop and implement an AI assessment and approval workflow through a hands-on project using Python.

Understand preventive, detective, and corrective AI controls, and how they connect risk, owners, tests, and evidence to keep systems safe and auditable.

Guardrails for prompts, outputs, tools, data access, agent actions, and workflow decisions govern AI systems in real time, with validation, escalation, and least-privilege controls.

Enforce comprehensive ai guardrails across prompts, outputs, tools, data access, and agent actions to prevent prompt injection, data leakage, and policy evasion.

Measure guardrail effectiveness by tracking triggers, blocks, escalations, bypasses, failures, and false positives to learn from incidents and drive continuous governance improvement.

Build a guardrails and policy enforcement service for AI governance using Python in a hands-on project.