Document current AI systems, their purposes and strategic importance. Identify all machine learning pipelines, generative models and inference engines in production or development. Understand business objectives, risk tolerance and compliance requirements that will shape your security approach.

Map the complete AI ecosystem—data sources, training pipelines, model architectures, deployment environments and integration points. Identify who has access to models and data, how systems are monitored and what controls currently exist. This creates a comprehensive inventory of assets requiring protection.

Assess exposure to adversarial attacks, model poisoning, data manipulation, prompt injection and other AI-specific threats. Test models for robustness against evasion techniques. Evaluate data integrity controls, training pipeline security and inference endpoint protections. Document existing gaps and potential attack vectors.

Determine the business consequences of different attack scenarios—from degraded model performance to compromised decision-making or regulatory breaches. Prioritise vulnerabilities based on likelihood, impact and exploitability. Identify quick wins alongside critical risks requiring immediate attention.

Design a phased programme to harden algorithms, implement anomaly detection, strengthen data governance and establish monitoring capabilities. Define specific initiatives, resource requirements, timelines and success metrics. Align security investments with business priorities and regulatory obligations.

Deploy technical controls - adversarial training, input validation, model versioning, access controls and continuous monitoring. Establish governance frameworks for model development, testing and deployment. Create incident response procedures for AI-specific threats. Build ongoing capability to detect, respond to and learn from security events.