February 16, 2026
08:00
Hilton DoubleTree West End
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This comprehensive 5-day programme equips technical, data, and compliance professionals with the skills needed to deploy large language models securely. You’ll explore risk assessment frameworks, secure prompt design, misuse detection, privacy considerations, and governance. Through real-world case studies, hands-on workshops, and policy development exercises, you’ll graduate with actionable strategies to safeguard LLM deployment in your organisation.
AI is powerful, but risky: Misuse of LLMs can lead to privacy breaches, bias, and reputational damage—organizations must implement strong safeguard strategies.
Regulatory scrutiny is growing: As AI regulations evolve, professionals must ensure deployments comply with emerging data protection, ethical, and security standards.
Trust equals adoption: Secure, trustworthy AI encourages adoption across teams, enabling organizations to reap benefits responsibly and sustainably.
Data scientists, machine learning engineers, and AI developers deploying or scaling LLMs
Compliance, privacy, and cybersecurity professionals overseeing AI governance and risk management
AI ethics officers, product owners, and tech leads integrating LLMs into enterprise workflows
IT architects and DevOps teams managing secure models, prompt pipelines, and prompt monitoring infrastructures
Over five intensive days, you will:
Assess and mitigate LLM risks – Identify misuse scenarios, adversarial prompts, and model vulnerabilities
Design secure prompts – Develop prompt architecture that minimizes leak risks and ensures safe model responses
Detect and prevent misuse – Build guardrails to catch harmful content, bias, and privacy violations
Embed privacy-by-design – Apply redaction, PII identification, and encryption in LLM input/output flows
Govern LLM rollout – Establish AI policies, approval workflows, and ethical use frameworks
Ensure AI explainability – Implement transparency techniques to trace model decisions
Prepare for regulation – Align practices with emerging AI safety standards and regulatory expectations
Deploy secure monitoring infrastructure – Set up logging, anomaly detection, and alerting systems
Develop and present an AI security roadmap – Create tailored strategies for safe LLM adoption in your context
Abdul, ML Engineer – Financial Services (5/5)
“The speaker was fantastic and I learned so much. I learned to build secure prompts that reduce risk of sensitive data exposure. I found the misuse detection workshops very useful, and I enjoyed the AI policy sessions. I’ll now implement prompt guardrails in our loan-processing model.”
Moosa, Privacy Officer – Healthcare Tech (5/5)
“This was a really brilliant course. The privacy-by-design modules taught me to embed redaction and encryption into our LLM workflows. I particularly enjoyed the case studies and plan to adjust our data pipelines accordingly.”
Lina, AI Governance Lead – Telecom (5/5)
“Very enjoyable and informative experience. I discovered how to manage LLM deployment ethically with policy frameworks. I found the monitoring infrastructure design highly valuable and will use these tools to build our AI compliance roadmap.”