AI can accelerate research and professional work, or quietly undermine its integrity. Defensive AI teaches you to capture the upside while protecting accuracy, accountability and trust.
Researchers, academics and professionals are already using AI every day, to search, summarise, draft, analyse and decide. The productivity gains are real. So are the risks: fabricated citations, unverified claims, undisclosed AI use, biased outputs, and decisions no one can properly account for.
Defensive AI is a practical stance, not a prohibition. It gives you the judgement and habits to know where AI genuinely helps, where it can harm, and how to keep your work defensible, to a reviewer, an editor, a regulator, or a court of professional opinion.
Map the research and professional workflow and identify the points where AI adds value versus where it introduces risk.
Practical methods for checking AI output, attributing sources, and disclosing AI use without compromising integrity.
An appropriateness test judged on impact and reversibility, so you apply more scrutiny exactly where it counts.
Lightweight guardrails and routines that survive contact with real deadlines and become part of how you work.
A high-impact session for a faculty, research group, conference or professional body, recently delivered at Excelsia University College.
A deeper, hands-on session where teams apply Defensive AI to their own workflows and build shared guardrails.
A self-paced course extending the masterclass, for individuals and cohorts. Register your interest to hear when it opens.
Defensive AI is built on the same appropriateness and risk-of-harm model embedded across my AI governance work, applied at the moment AI is engaged for a task, not message by message.
High-stakes, hard-to-reverse work earns more scrutiny; routine, reversible work can move faster.
Book a masterclass or workshop, or register your interest in the online course.
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