• integrated_82#Q4.12

RAIDT can be used in cybersecurity by treating each alert-handling event or incident-support interaction as a governed run, rather than relying on generic policy statements or model-level documentation alone. In the empirical RAIDT paper, the cybersecurity vignette is alert triage: a security analyst uses GenAI to recommend incident next steps, and the key question is whether those recommendations are stable, reviewable, and evidence-linked. RAIDT therefore operationalises the run as the unit of governance through a run-level evidence pack that records the prompt or template, model and tool settings, retrieved threat-intelligence context where used, outputs, integrity checks, and reviewer actions. That record is then scored as a score profile across the five pillars (Responsibility, Auditability, Interpretability, Dependability, Traceability), using anchors 1=missing / 3=partial / 5=audit-ready. For cybersecurity, this is especially valuable because dependability, auditability, and secure traceability are not abstract aspirations: they determine whether triage advice can be reconstructed, challenged, and safely reused during post-incident review.

RAIDT is also useful because it treats influence methods as governance interventions. In practice, a security workflow may combine structured prompting, retrieval from an approved threat-intelligence repository, and preference constraints that discourage invented indicators or overconfident recommendations. The empirical paper argues that stacked configurations can improve evidence coverage, while RAG raises auditability and traceability when retrieval snapshots are retained, and repeat-run testing makes variance visible for the dependability pillar. The sector playbook adds the operational discipline needed in high-risk settings: each run should carry stable identifiers, timestamps, operator or reviewer IDs, versioned artefacts, and evidence-linked reviewer notes, so low scores can trigger concrete remediation such as better logging, stricter escalation thresholds, or human review for ambiguous alerts. Used this way, RAIDT supports incident response, internal assurance, and stakeholder communication without claiming that the model is correct merely because it is fluent.

Consider a hospital security operations centre using GenAI to triage a suspicious email campaign that may indicate ransomware activity. The analyst submits the alert bundle, the system retrieves only approved internal playbooks and threat-intelligence notes, and the model returns a prioritised recommendation: isolate the affected mailbox, check lateral movement indicators, and escalate to the incident lead because confidence is moderate. Under RAIDT, that output is not accepted as a free-standing suggestion; the organisation stores the run-level evidence pack, including the run ID, prompt version, retrieved passages, model configuration, output hash, and reviewer comments.

The team then scores the run. Dependability is judged by repeating the same case under fixed settings to see whether the recommendation remains stable. Auditability and Traceability depend on whether the retrieved sources and configuration can be replayed later. Responsibility depends on whether escalation triggers and uncertainty are explicit. If the run scores low on any pillar, the workflow is changed before wider deployment, for example by tightening the prompt, improving retrieval logging, or requiring mandatory human sign-off for similar alerts.

• 09-RAIDT_Empirical_M_V50.docx
• 21-RAIDT_Sector_Playbook_Healthcare_V2