You know your cybersecurity maturity—you've probably done a CMMC assessment or used NIST Cybersecurity Framework. You know where you stand on network security, endpoint protection, and incident response.
But do you know your AI security maturity?
Most organisations don't. They're deploying AI systems with ad-hoc governance, no formal risk assessment, and minimal security controls. They'll find out they're immature only when an incident occurs.
This framework helps you assess your AI security posture honestly and build a roadmap to improve it.
The AI Security Maturity Model: Five Levels
Level 1: Ad Hoc
AI systems are deployed with minimal formal governance. Security controls are reactive. Models come from sources without vetting. Testing is limited. Incident response for AI doesn't exist. Data handling is inconsistent. No formal monitoring of model behaviour.
Examples: Pilot projects using ChatGPT or open-source models without approval. Ad-hoc fine-tuning. No audit trails.
Level 2: Managed
AI governance policies are documented. Model inventory exists. Risk assessments are conducted (but not comprehensively). Basic security controls are in place: input validation, access controls, basic testing. Training data is protected. Incidents are logged. Monitoring for obvious anomalies.
Examples: Model approval checklist. Dependency scanning. Basic access control. Incident log. Ad-hoc monitoring dashboards.
Level 3: Defined
AI security processes are formalised and documented. Model provenance is tracked. Comprehensive risk assessment and threat modelling. Third-party models are vetted. Supply chain controls are in place. Continuous monitoring of model performance. Regular security testing (red teaming, adversarial testing). Documented incident response procedures for AI.
Examples: SBOM for all models. Model signing and hash verification. Continuous validation testing. Baseline behaviour monitoring. AI-specific incident playbooks.
Level 4: Quantified
AI security metrics are defined and measured. Maturity is tracked across dimensions (governance, technical controls, monitoring). Predictive monitoring detects anomalies before impact. Privacy controls are deeply integrated. Differential privacy in sensitive models. Formal vulnerability disclosure process. Regular compliance assessments. AI security is integrated into business risk management.
Examples: Quantified KPIs for AI security. Predictive drift detection. Differential privacy implementation. Vulnerability disclosure program for AI.
Level 5: Optimised
AI security is optimised continuously. Proactive threat hunting. Advanced adversarial robustness testing. Automated governance and compliance. AI systems are intrinsically secure by design. Third-party model ecosystems are vetted and trusted. Continuous innovation in AI security controls. Security drives AI architecture decisions.
Examples: Zero-trust architecture for AI. Automated compliance and governance. Advanced red teaming. Certified AI security suppliers. Continuous security improvement.
Self-Assessment Framework
Assess your organisation across six dimensions:
1. Governance and Policy
- Does your organisation have a formal AI governance policy?
- Is there documented approval process for deploying models?
- Are roles and responsibilities for AI security clear?
- Are AI security metrics defined and tracked?
- Is AI governance integrated into board/executive oversight?
2. Model Provenance and Supply Chain
- Do you have an inventory of all models in use?
- Do you vet third-party models before use?
- Are dependencies tracked? Is SBOM maintained?
- Are models signed and cryptographically verified?
- Is supply chain risk formally assessed?
3. Data Security and Privacy
- Is training data protected (encryption, access control)?
- Are privacy impacts assessed before deployment?
- Are controls in place to prevent PII leakage?
- Is data classification implemented?
- Are differential privacy techniques applied where needed?
4. Model Integrity and Validation
- Is model integrity verified before deployment?
- Are models continuously tested against known test cases?
- Are models red-teamed for adversarial robustness?
- Is model behaviour monitored in production?
- Can you detect drift, tampering, and degradation?
5. Security Operations and Incident Response
- Are there AI-specific incident response procedures?
- Is there continuous monitoring of model outputs?
- Can you detect and contain compromised models?
- Are audit logs maintained for all model access?
- Are incidents reviewed and lessons learned captured?
6. Compliance and Audit
- Do AI systems comply with regulatory requirements (Privacy Act, APRA, etc.)?
- Are compliance assessments conducted regularly?
- Can you demonstrate compliance to auditors?
- Are audit trails maintained?
- Is there independent assurance of AI security?
Common Regression Patterns
As you progress, watch out for these regression patterns:
Speed Regression
You implement rigorous controls that slow down model development. Teams get frustrated and find workarounds. Suddenly you're back to Level 1 (but with the appearance of Level 3).
Prevention: Design controls that enable fast, safe development. Use automation to reduce manual overhead.
Scope Creep Regression
You implement Level 3 controls for critical systems. Then new models are deployed outside the governance process, claiming they're "experimental" or "non-critical".
Prevention: Define "in scope" clearly. All models in production, even small ones, go through governance.
Turnover Regression
Your AI security team leads leave. New team members aren't fully trained. Procedures aren't followed consistently. Maturity declines quietly.
Prevention: Document everything. Build continuous training into your AI security programme.
Tool Regression
You use a tool to scan models for vulnerabilities. The tool breaks with a new model architecture. Instead of fixing it, teams work around it. Tool becomes useless. Monitoring degrades.
Prevention: Invest in maintainable tools. Have alternatives ready if primary tool fails.
Roadmap: Getting from Where You Are to Where You Need to Be
Year 1: Foundation (Levels 1→2)
- Assess current state honestly
- Create AI governance policy
- Build model inventory
- Implement basic approval process
- Scan dependencies for known vulnerabilities
- Implement basic access control
- Create incident logging system
Year 2: Formalisation (Levels 2→3)
- Formalise risk assessment and threat modelling
- Implement model signing and verification
- Build continuous validation testing framework
- Establish baseline monitoring for model behaviour
- Create AI-specific incident response playbooks
- Implement privacy impact assessments
- Vet third-party models formally
Year 3+: Maturity (Levels 3→4/5)
- Define and measure AI security metrics
- Implement advanced monitoring and anomaly detection
- Integrate differential privacy where needed
- Conduct regular red teaming and adversarial testing
- Automate compliance and governance
- Integrate AI security into architecture and design
Australian Context
For Australian organisations:
- Government and critical infrastructure: Target at least Level 3 (essential controls). ASIO/ASD will expect this.
- Financial services: APRA is watching. Target Level 3+ for regulated AI systems.
- All organisations: Expect regulatory requirements for AI governance to tighten. Build to Level 3 at minimum.
Key Takeaways
- Maturity models help you understand where you stand and where you need to go
- Most organisations are at Level 1 or 2. This is not sustainable long-term.
- Target at least Level 3 for any AI system in production handling sensitive data
- Progression takes time. Focus on foundational controls first.
- Avoid regression by building sustainable processes and investing in people
- For Australian organisations, regulatory expectations are rising. Build maturity proactively.