4CRisk.ai has published a new guide on deploying an AI governance programme in 2026, with Supradeep Appikonda, COO and co-founder, outlining what organisations need to do to stay aligned with AI regulations, rules and standards. The piece positions governance as a practical programme that goes beyond defining AI strategy and principles, and instead extends into AI model governance and technical monitoring—particularly where vendors are involved—to demonstrate adherence to internal policies.
The guide says organisations are now focusing on more than showing their governance strategy and framework of principles and policies comply with regulation (such as the EU AI Act), rules (including federal or state-level requirements), and standards (including NIST or ISO). It argues programmes are being extended to incorporate impact assessments and technical monitoring, so teams can evidence that both in-house and third-party AI products comply with internal procedures and controls.
In describing the operational step up, Appikonda compares the challenge to “third-party or vendor risk management on steroids”, but with a specific focus on AI compliance. Appikonda notes that while many concepts will already be familiar from AI policy work, a “truly robust AI Governance program” depends on risk tiering and assessments, regular monitoring of AI models, and the ability to close gaps with evidence that holds up under scrutiny.
According to Appikonda, most organisations will have already clarified roles and responsibilities and built accountability models that may include steering committees, working groups, training teams and, in some cases, an AI centre of excellence. The next step is ensuring teams can execute the tasks required for AI governance in practice.
One challenge highlighted is that the information required for governance often sits across multiple internal sources—such as RFPs, proposals, contracts, vendor disclosures and attestations—or may be “buried” in outputs from technical pilots. This can make maturity difficult, even when principles and risk tiering are already defined.
Appikonda breaks foundational work into several activity areas. On AI principles, it says organisations should define what trustworthiness, transparency, fairness, bias and accountability mean in their context. It also points to bias mitigation procedures and protocols designed to identify and reduce algorithmic bias, and highlights the need for human-in-the-loop points where human intervention and oversight are required.
On risk tiering, categorisation and impact assessments, it says working groups should classify AI use cases by risk tier—such as unacceptable, high, limited, or minimal risk—and validate these classifications with both IT and business leadership. It also recommends Algorithmic Impact Assessments before deploying new models, with checks for drift and anomalies, and flags security risks including spoofing attempts, data poisoning, infringement and hallucinations.
For AI model governance, Appikonda focuses on data management and lineage (including where data comes from, how it is labelled, and how it is used), privacy compliance aligned to requirements such as GDPR and CCPA, and quality control audits covering accuracy, completeness, bias and representativeness. For technical monitoring, it highlights explainability (XAI) to support clear reasoning and guardrails, performance tracking for “model drift,” logging and documentation of model versions and testing, and confidence controls to define boundaries and off-limit topics, demonstrate thinking/logic, and include confidence levels in outputs.
Appikonda then sets out a step-by-step process for scaling governance and adoption. It starts with automated regulatory change management supported by intelligent horizon scans, including mapping regulatory changes to internal risks, systems, business units, policies and controls.
Next, it recommends scanning internal policies and harmonising controls, using tooling to highlight which policy or control needs updating, while reducing duplicated or overlapping controls. A third step focuses on vendor alignment, including scanning vendor disclosures against a unified internal framework, with the approach described as “test once, comply many, report across,” to reduce repeat testing.
Step four centres on continuous model governance and monitoring across internal systems and vendor version changes, using structured analysis, mapping to obligations and controls, and documenting outcomes with a full audit trail. Step five covers faster answers and reporting for stakeholders, with AI used to draft and assemble reporting inputs, while humans finalise the output.
For more insights, read the full guide here.
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