Chitraj (Raj) Singh
Behavioural scientist | Founder & CEO, MindHug | Former Bank of England/PRA regulator
London, United Kingdom
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Raj Singh is a behavioural scientist, economist and data scientist, and Founder and CEO of MindHug. A former Bank of England/PRA Senior Regulatory Supervisor, he has over a decade experience in reviewing governance, risk, data quality and internal models at 20+ major financial institutions. Through MindHug, his focus is on helping institutions get ready for the future of work through improving psychological safety and skill-based capabilities. He has delivered three Innovate UK-funded programmes, including DSIT/Innovate UK Cyber Local on AI-enabled risks and the psychological and behavioural barriers to cybersecurity and AI adoption. Raj is a former Discribe Hub+ research partner within UKRI's Digital Security by Design programme alongside Microsoft, Airbus, HSBC and RSA Security. He is a Lead Trainer and Guest Lecturer at LSE, teaches at UCL School of Management and sits on its International Advisory Board. He was named one of 20 LSE alumni shaping the world.
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Think Like a Hacker, Check Like a Human: An AI and Digital Security Workshop
A hands-on session where participants put AI tools under pressure and watch how human behaviour shapes what comes back.
Working in small groups, participants use everyday AI assistants on realistic security scenarios: assessing a suspicious message, reviewing an AI-generated recommendation, and deciding what to trust. Each round surfaces a different human vulnerability: how framing steers the output, how a confident first answer anchors every answer after it, and how social engineers exploit the same shortcuts in people that AI reproduces at scale.
We then rebuild each scenario with simple behavioural checks: stating priors before the prompt, testing for confirmation bias after the first answer, and building human-review points that people will actually use. The session draws on the AI and Digital Security Interaction Workshop delivered at LSE in August 2026 (52 participants, 95% met their goals) and on MindHug's CPD-accredited digital-security training for DSIT/Innovate UK Cyber Local.
Participants leave with a reusable checklist for AI-assisted security decisions and a clearer view of where their own team's blind spots are.
Bring a laptop with access to any mainstream AI assistant (ChatGPT, Copilot, Claude or Gemini).
AI as a Mirror: The Human Vulnerabilities Hiding in Your AI-Assisted Security
Security teams are adopting AI for triage, threat modelling, code review and phishing defence. Most of the conversation is about securing the model. This talk is about the other attack surface: the people using it.
AI is a mirror with two sides. First, it reflects and amplifies us. The way an analyst frames a prompt, the assumption they bring to an alert, the answer they hoped to see: AI hands these back with fluency and confidence, and confirmation bias quietly becomes part of the control. Second, even when we do everything right, AI may not. Models learn from vast amounts of human data and behaviour that was not always right, so their outputs are the most likely answer, not necessarily the one your situation needs.
There is a third problem anyone with a risk background will recognise. Most risk models assume the past predicts the future. AI is now shaping how people search, decide and click, creating behaviours and data that are not yet visible in our historical baselines. That gap is exactly where social engineers operate.
Drawing on behavioural science, and years reviewing risk models as a Bank of England/PRA supervisor, and MindHug's work on digital-security adoption for UK Government and within UKRI's Digital Security by Design programme, this session covers:
- where human bias enters AI-assisted security workflows, before and after the prompt
- why psychological safety decides whether people report an AI mistake or quietly work around it
- practical checks for framing, confirmation bias and bias carry-forward that teams can use straight away
You will leave with a simple review pattern for AI-assisted security decisions, and a sharper sense of which "AI errors" are really human ones.
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