Why I Write About AI Governance (And Why It Actually Matters)

I have spent the last two decades sitting in rooms where smart people make expensive mistakes with technology they do not fully understand.

I have watched boards approve AI initiatives without asking basic questions about data lineage, monitoring, and accountability.

I have seen compliance teams try to retrofit controls onto systems that were already in production, with customers already affected.

I have also debugged Monte Carlo risk models at 2 AM because someone assumed “AI risk” was just another flavor of traditional IT risk.

This blog exists because I got tired of watching the same failures repeat.

Most AI governance content falls into two categories that do not help you when the pressure is real.

It is either academic work that never reaches the operating model, or vendor content that sounds confident but collapses when you ask, “What evidence would an auditor accept?”

I write for the person who has to defend decisions, not just describe them.

If you are the risk manager who just inherited AI oversight with zero training, I know what that feels like.

If you are the compliance officer trying to determine whether the EU AI Act applies to your “simple chatbot,” I have been in that conversation.

If you are an internal auditor asked to validate a machine learning model and you do not know Python, you are not alone.

If you are a Chief AI Officer hired to “govern AI responsibly” but given no budget and a six‑month deadline, you have a structural problem, not a motivation problem.

If you need practical frameworks that survive contact with reality, not aspirational principles that fall apart under audit, you are in the right place.

What I mean by “AI governance” (in plain terms)

I do not treat AI governance as an ethics essay.

I treat it as the operating system that makes AI systems deployable, auditable, and recoverable.

In practice, that means answering questions like these with evidence:

Who owns this AI system in production, and who can pause it?

What data trained it, and what data is it using today?

What controls stop it from leaking confidential information?

How do we detect model drift, performance decay, bias shifts, or unsafe behavior after release?

What is the incident playbook when it fails at scale?

If you cannot answer those questions, you do not have governance. You have activity.

Where AI governance collides with AI development

AI systems do not fail like traditional software.

Software is mostly deterministic. You ship code, it behaves as written.

AI systems are probabilistic and data-dependent. You ship code plus a model plus a moving data environment, and behavior changes even when the code stays the same.

That is why “approval at launch” is weak control design.

In the real world, governance has to plug into the AI delivery pipeline, not sit beside it.

Here is the lifecycle I anchor most programs on:

Data → Training → Validation → Deployment → Monitoring → Change control → Retirement

If your controls only exist at “Validation,” you will miss most failures that occur after deployment.

Common failure patterns I keep seeing (and why they are expensive)

Teams build a model that performs well in a notebook, then discover they have no ModelOps or MLOps path to deploy it safely.

Monitoring is limited to uptime and latency, while the real risk is silent performance degradation, drift, or a shift in user behavior.

Third-party AI is onboarded through procurement as if it were a normal SaaS tool, without vendor evaluation on training data use, model change notifications, or audit rights.

Controls exist as documents, but they are not enforced by pipelines. No gating tests, no versioning discipline, no evidence trail.

The organization cannot produce an inventory of AI systems in production, so it cannot manage what it cannot see.

What you will actually find here

This is not a blog about “trust” as a slogan.

It is a working notebook of governance mechanisms that hold up under executive pressure, regulatory scrutiny, and operational incidents.

You will find implementation guidance that assumes real constraints: limited budget, skeptical stakeholders, legacy systems, and teams who want to ship.

You will also find technical content that bridges governance with development practices, including monitoring, testing, validation, and evidence generation.

In particular, I publish:

Practical implementation guides for standards such as ISO/IEC 42001, ISO/IEC 23894, and EU AI Act aligned governance approaches.

Quantitative risk models in Python and R that translate “this might be biased” into “this is the probable financial exposure under defined scenarios.”

Failure stories from real projects, including the controls that did not work, the assumptions that were wrong, and the fixes that survived audit and remediation cycles.

My bias as a practitioner

I am slightly impatient with governance that cannot be tested.

If a control cannot produce evidence, it is not a control. It is a sentence.

If a policy cannot be operationalized into build gates, monitoring checks, and incident routines, it is not governance. It is shelf decoration.

That is the perspective behind everything I publish.

A technical example of what “governance in the pipeline” looks like

When I say governance should be real, I mean it should show up in the same places your engineers already work.

For example, a release gate that blocks deployment if minimum evidence is missing:

release_gates:
  - name: model_card_required
    rule: "model_card.exists == true"

  - name: monitoring_required
    rule: "monitoring.drift.enabled == true AND monitoring.performance.enabled == true"

  - name: high_risk_extra_checks
    rule: "if risk_tier == 'high' then fairness_test.passed == true AND human_override.enabled == true"

This is not about bureaucracy.

This is about preventing the most common enterprise failure mode: shipping an AI system that nobody can explain, monitor, or shut down safely.

Published articles and practical guides

Below is a curated index of articles. Each one is designed to solve a specific friction point I keep seeing in enterprise AI.

If you are time-poor, skip to the domain that matches your current pain.

AI governance frameworks and standards

Practical ISO/IEC 42001 Implementation Guide

A step-by-step approach to implementing an AI Management System. I focus on governance structure, control design, documentation, audit readiness, and how to integrate this with existing GRC.

How to Actually Use ISO/IEC 23894 for AI Risk Management

A practical playbook for operationalizing AI risk management. Less philosophy, more workflow, scenario libraries, and monitoring expectations.

A 12-Step Procedure Merging ISO 27005, ISO 23894, ISO 42001, and FAIR

An integrated risk method that teams can execute without turning the process into a six-month consulting project.

Implementation Tips for ISO/IEC 42005 AI Impact Assessments

How to run impact assessments that produce usable outputs: stakeholder mapping, scoring, mitigations, and documentation that stands up in review.

Practical Implementation Tips for AI Project Alignment

How to align AI work with strategy and risk appetite so you do not end up with technically strong projects that deliver weak enterprise value.

Chief AI Officer (CAIO) operating model and accountability

What a Chief AI Officer Actually Owns, and What Should Stay With Risk, Legal, and IT

A practical CAIO responsibility map across governance, operational assurance, organizational enablement, and strategic influence, aligned to three lines of defense.

AI risk assessment and quantification

AI Risk Modeling: Beyond “Is AI Accurate?”

How I quantify AI exposure using frequency-severity logic, scenario analysis, and loss distributions, then connect it to board-level risk language.

The AI Risk Taxonomy Most Organizations Never Build

A taxonomy approach that prevents the “one heat map to rule them all” problem.

The AI Loss Taxonomy Your Risk Assessments Are Missing

A structured way to think about loss: direct financial, regulatory, litigation, reputational, churn, and operational disruption.

Practical AI Assessments: Risk, Impact, and Feasibility

A combined assessment workflow that produces a decision, not just a report.

Implementation Tips for Expert Calibration and AI-Augmented Risk Estimation

How to reduce “confident guessing” in risk scoring and produce estimates you can defend.

AI security, threat modeling, and red teaming

The 45 AI Threat Vectors Your Security Team Probably Isn’t Tracking

A threat taxonomy that includes data poisoning, model extraction, prompt injection, membership inference, backdoors, and supply chain risks.

AI Threat and Vulnerability Assessment Framework

A structured approach to AI threat modeling and vulnerability assessment, designed to be run repeatedly, not once.

Practical AI Red Team Implementation Tips for Safer, More Resilient AI Systems

How to stand up an AI red team, what scenarios to test, how to document results, and how to drive remediation that actually sticks.

Guide to AI Agent Risk and Control Management Across the Full Lifecycle

Agents raise the stakes because they can take actions, not just generate text. This guide focuses on delegation limits, human-in-the-loop design, monitoring, and liability.

Quantitative risk modeling and predictive analytics

Quantitative Risk Assessment Using Monte Carlo Simulations and Convolution Methods in R

Executable methods for compound loss modeling, loss exceedance curves, reserves, and sensitivity analysis.

Machine Learning for Advanced Predictive Risk Modeling

How to use supervised learning for risk prediction responsibly, including validation and explainability.

Predictive Risk Model That Makes the Fewest Expensive Mistakes

Cost-sensitive modeling. Because accuracy is rarely the business objective.

How to Explain AI Risk Models So Regulators Actually Trust Them

A communication framework for regulators, auditors, and boards, anchored in assumptions, sensitivity, limitations, and evidence.

AI project management and delivery (where good ideas die)

Field Guide to the 8 Factors That Determine Success or Failure of AI Projects

A practical view of why AI programs succeed or stall: sponsorship, data maturity, team design, and operating model.

Practical Fixes for Why Data Science Projects Fail

Root causes and fixes that reduce rework and prevent “pilot purgatory.”

Managing AI Development and Deployment Projects

A disciplined approach that respects the exploration phase but still gets to production with control.

Managing AI Projects with Agile Exploration and MLOps

How I combine experimentation with release discipline so governance does not become the enemy of shipping.

How to Build the Right AI Delivery Team

Roles, responsibilities, and why missing a single capability (like platform engineering or domain expertise) can break delivery.

Why Separating Your AI Build Team from Your AI Ops Team Guarantees Failure

An organizational design problem disguised as a tooling problem.

Resource Estimation for AI Projects

A reality-based way to estimate compute, people, data effort, and vendor spend.

Goal Setting for AI Projects

How to set measurable AI goals that include constraints, not just targets.

Feasibility Assessment for AI Projects

Technical feasibility, economic feasibility, operational feasibility, and regulatory feasibility, evaluated upfront.

AI monitoring, validation, and maintenance (where governance becomes real)

Model Selection and Validation for AI Projects

How to choose models and prove they generalize, including cross-validation and holdout discipline.

The Model Robustness and Monitoring Playbook

Drift detection, degradation triggers, and what to monitor beyond accuracy.

Practical Monitoring and Evaluation for AI Projects

A full monitoring architecture: technical metrics, model metrics, business metrics, and governance metrics.

Practical KPI Tracking for AI Projects

Leading and lagging indicators that let you intervene before failure becomes visible to customers.

Practical Post-Deployment Maintenance for AI Systems

Versioning, retraining cadence, dependency updates, security patching, and retirement discipline.

AI Deployment Governance for Feedback Loops and MLOps

Controls for the feedback loop so you can improve systems without creating uncontrolled change risk.

Spent 5 Years Validating Enterprise AI Models: Here’s What I Learned

Common validation failures, regulator expectations, documentation patterns, and what breaks most often in production.

How to use this index (fast)

If you are building an AI governance program from scratch, start with ISO/IEC 42001 and the CAIO responsibilities map, then move into monitoring and incident readiness.

If you are preparing for audit or regulatory scrutiny, focus on evidence artifacts: inventory, model documentation, monitoring records, change logs, and vendor governance.

If you are a technical lead trying to ship responsibly, start with the MLOps governance, monitoring, and security testing articles. That is where most “surprises” hide.

Closing

I do not write to sound smart.

I write because AI governance fails quietly until it fails loudly, and by then, the people in risk, compliance, and audit are the ones asked to explain what happened.

If you want a specific topic covered next, tell me what you are being asked to govern this quarter: customer-facing models, internal copilots, vendor AI, or autonomous agents.

AI Policy, Compliance, and Regulatory Frameworks
Responsible AI Policy Categories and Implementation Framework
https://hernanhuwyler.wordpress.com/responsible-ai-policy-categories/
Taxonomy of responsible AI policies covering ethics, fairness, transparency, accountability, privacy, security, safety, and human oversight. Includes policy templates, implementation checklists, training programs, and compliance verification protocols.
Rules for AI Use: Accountability, BYOAI, Safety by Design, and Content Provenance
https://hernanhuwyler.wordpress.com/rules-for-ai-use-accountability-byoai-safety-by-design-and-content-provenance/
Corporate policy framework governing employee AI usage including bring-your-own-AI (BYOAI) protocols, accountability assignments, safety-by-design requirements, and content provenance tracking for generative AI outputs.
Practical CAIO Responsibilities: What Chief AI Officers Actually Do
https://hernanhuwyler.wordpress.com/practical-caio-responsibilities/
Role definition for Chief AI Officer positions including strategic responsibilities (AI roadmap, portfolio governance), operational responsibilities (project oversight, resource allocation), and assurance responsibilities (risk management, regulatory compliance, board reporting).
Compliance Controls for AI Systems
https://hernanhuwyler.wordpress.com/compliance-controls-for-ai/
Control catalog mapping AI-specific compliance requirements to implementable controls across data governance, model development, deployment, monitoring, and documentation domains. Aligned with EU AI Act, GDPR, sector-specific regulations.
Practical Implementation Tips for Building and Maintaining an AI Compliance Register
https://hernanhuwyler.wordpress.com/practical-implementation-tips-for-building-and-maintaining-an-ai-compliance-register/
Operational guidance for constructing AI compliance registers tracking regulatory obligations, control mappings, evidence collection, audit trails, and compliance status reporting across multiple jurisdictions.
Practical Implementation Tips for AI Fundamental Rights Taxonomy
https://hernanhuwyler.wordpress.com/practical-implementation-tips-for-an-ai-fundamental-rights-taxonomy/
Framework for identifying and assessing fundamental rights impacts of AI systems as required by EU AI Act. Covers rights taxonomy, impact assessment methodologies, mitigation planning, and stakeholder consultation protocols.
Practical Implementation Tips for Fundamental Rights Impact Assessment for High-Risk AI Systems
https://hernanhuwyler.wordpress.com/practical-implementation-tips-for-a-fundamental-rights-impact-assessment-for-high-risk-ai-systems/
Step-by-step procedure for conducting fundamental rights impact assessments (FRIA) for high-risk AI systems under EU AI Act Article 27. Includes assessment templates, stakeholder engagement protocols, impact scoring, mitigation planning, and documentation requirements.
Modeling Practices for Regulated AI Systems
https://hernanhuwyler.wordpress.com/modeling-practices-for-regulated-ai/
Best practices for developing AI models in regulated industries (financial services, healthcare, critical infrastructure) covering model governance, validation standards, documentation requirements, change control, and regulatory submission protocols.

AI Procurement and Vendor Management
AI Procurement Controls and Vendor Risk Management
https://hernanhuwyler.wordpress.com/ai-procurement-controls/
Comprehensive framework for procuring AI systems and services including vendor assessment criteria, technical due diligence protocols, contractual protections, service level agreements, audit rights, data handling requirements, and ongoing vendor monitoring.
How to Negotiate AI Agreements That Protect Data, Value, and Liability
https://hernanhuwyler.wordpress.com/how-to-negotiate-ai-agreements-that-protect-data-value-and-liability/
Legal and commercial negotiation strategies for AI vendor contracts covering intellectual property rights, data ownership, model performance warranties, liability caps, indemnification clauses, termination rights, and regulatory compliance responsibilities.