Plugging GenAI and Agents Into Your Existing Governance, Not Bolting Them On

Organisations often err by treating AI governance separately from their existing frameworks, leading to confusion and inefficiency. Instead, they should incorporate AI into current governance structures, expanding their scope and utilising existing processes. By aligning AI oversight with established practices, organisations can streamline governance without unnecessary duplication.

Aligning DASUD With AI Regulations and Frameworks

As AI regulations develop, effective lifecycle mapping to frameworks is essential. By utilising the DASUD approach, organisations can demonstrate compliance with risk management, data governance, and oversight expectations. Each DASUD stage, from design to deletion, aligns with regulatory requirements, making AI governance clearer and more defensible, ultimately gaining stakeholder support.

DASUD on a Loop: Governing Continuous‑Learning Agents and Feedback

Governance of continuous-learning agents requires a structured approach using the DASUD framework. This involves defining allowed learning types, acquiring and validating feedback, maintaining version control, deploying changes cautiously, and ensuring mechanisms for rollback when needed. Establishing clear boundaries and monitoring is vital to prevent harmful insights from influencing system behaviour.

When Advanced AI Goes Wrong: Incident Response Under DASUD

Most organisations need tailored incident response plans for AI-related issues, including harmful outputs and misuse of data, alongside traditional incident types. Establishing specific scenarios, clear reporting pathways, and effective logging is crucial. Organisations should prepare for incidents proactively, fostering a feedback loop to refine responses and enhance overall safety and governance.

Human‑in‑the‑Loop, Human‑on‑the‑Loop: Choosing the Right Oversight Model

Effective AI governance hinges on explicit oversight modes, including Human-in-the-loop (HITL), Human-on-the-loop (HOTL), and automated systems. Each mode serves distinct use cases based on impact level. Proper documentation, data acquisition, and structured workflows are essential to ensure accountability and transparency, moving beyond vague assurances of human involvement.