AI Security Roadmap

A practical path forward

Securing AI isn’t a one-off project. It’s a journey that requires sustained commitment, clear priorities, and a willingness to adapt as both the technology and threat landscape evolve. This roadmap provides a principled, practical framework for that journey, grounded in what works at enterprise scale rather than theoretical abstractions.

Each phase builds on the last. Resist the temptation to skip ahead.

Phase 1: Assessment and Planning

Before you can secure AI, you need to understand what you’re securing and what you’re securing it against.

Know your AI estate

Most organisations don’t have a comprehensive inventory of their AI systems. Shadow AI (models and tools adopted without IT or security oversight) is endemic. Start here:

• Inventory all AI systems in use, development, and procurement, including third-party and embedded AI
• Classify by risk based on data sensitivity, decision impact, and operational criticality
• Map data flows from training data sources through model deployment to inference outputs
• Identify owners and establish clear accountability for each AI system

Assess your threat landscape

Generic threat assessments won’t suffice. AI systems face specific threats that require specific analysis:

• Adversarial attacks: can inputs be crafted to manipulate model behaviour?
• Data poisoning: how robust are your training data pipelines?
• Model theft: are model weights and architectures adequately protected?
• Prompt injection: for LLM-based systems, how resilient are they to prompt manipulation?
• Supply chain risks: what’s the provenance of your pre-trained models and dependencies?

Establish governance foundations

Security without governance is chaos. Governance without security is theatre:

• Define AI security policy aligned with existing information security frameworks
• Establish roles and responsibilities: who owns AI security decisions?
• Set risk appetite: what level of AI risk is your organisation willing to accept?
• Align with regulation: NIST AI RMF, EU AI Act, ISO/IEC 42001, and sector-specific requirements

Phase 2: Build Foundation

With a clear picture of your AI estate and threat landscape, you can start building the security foundations.

Technical controls

• Access management for models, training data, and inference endpoints
• Input validation and sanitisation for all AI system interfaces
• Output monitoring for anomalous, biased, or potentially harmful results
• Model versioning and integrity: cryptographic hashing of model weights, reproducible training pipelines
• Secure development practices integrated into the AI/ML development lifecycle

People and culture

• Security awareness training tailored to AI teams, not generic phishing training, but AI-specific threat scenarios
• Cross-functional collaboration between security, data science, and business stakeholders
• Clear escalation paths for AI security concerns
• Embed security champions within AI development teams

Process and governance

• AI security review gates in the development and deployment pipeline
• Incident response playbooks for AI-specific scenarios
• Third-party risk management for AI vendors and model providers
• Regular reporting to leadership on AI security posture

Phase 3: Implement and Integrate

Foundations in place, the focus shifts to embedding AI security into operational reality.

Continuous monitoring

Static assessments aren’t enough. AI systems change over time, through retraining, fine-tuning, or simply through distribution shift in the data they process:

• Monitor model performance for drift that could indicate compromise or degradation
• Log and audit all interactions with AI systems, particularly privileged operations
• Detect anomalous patterns in AI system behaviour that could indicate adversarial activity
• Automate where possible: manual monitoring won’t scale with AI deployment

Security operations integration

AI security shouldn’t exist in a silo. Integrate it into your existing security operations:

• Extend your SOC to cover AI-specific events and alerts
• Update threat intelligence to include AI-relevant indicators of compromise
• Integrate AI security events into your SIEM and security orchestration platforms
• Conduct tabletop exercises with AI security scenarios

Supply chain security

As AI supply chains grow more complex, security must keep pace:

• Assess all third-party AI components against your security requirements
• Establish contractual security obligations with AI vendors
• Monitor for vulnerabilities in AI frameworks, libraries, and pre-trained models
• Maintain bill of materials for AI systems, including data provenance

Phase 4: Strategic Transformation

The final phase is about moving from defensive security to strategic advantage.

Security as enabler

Mature AI security isn’t a cost centre; it’s a competitive advantage:

• Build trust with customers, regulators, and partners through demonstrable AI security
• Accelerate innovation by providing secure guardrails that enable responsible experimentation
• Reduce cost of incidents through prevention rather than response
• Attract talent: the best AI practitioners want to work for organisations that take security seriously

Continuous improvement

The threat landscape evolves. Your security must evolve with it:

• Regular red-teaming of AI systems, including adversarial machine learning attacks
• Benchmark against emerging standards and best practices
• Share lessons learned with the broader community, because security improves for everyone when knowledge is shared
• Invest in research: support academic and industry research into AI security

Leadership and governance maturity

• Board-level reporting on AI security risk and posture
• Integration with enterprise risk management: AI security as part of the broader risk picture
• Influence policy: engage with regulators and standards bodies to shape the governance landscape
• Build a security culture that permeates every aspect of AI development and deployment

The journey, not the destination

There is no final state of AI security. The technology will continue to evolve, threats will continue to emerge, and governance will continue to mature. What matters is having a principled framework for navigating this evolution, and the discipline to follow it.

Start where you are. Use what you have. Build from the fundamentals. And remember: the organisations that secure AI effectively will be those that approach it with mission-driven purpose, collaborative spirit, and the humility to keep learning.