AI & Data
Best Practice: Conduct regular security reviews and threat modelling for AI pipelines
Sep 12, 2024
AI systems are vulnerable to various security threats, including adversarial attacks, data poisoning, and model inversion attacks. Regular security reviews and threat modelling help identify these risks early and implement mitigations, ensuring that AI pipelines are secure and resilient to potential threats.
Why Security Reviews and Threat Modelling Matter
- Identify security vulnerabilities: Threat modelling uncovers potential weaknesses in the AI pipeline, such as exposed attack surfaces or inadequate data handling procedures. Regular security reviews ensure these vulnerabilities are addressed promptly.
- Mitigate adversarial attacks: AI models are susceptible to adversarial attacks, where small perturbations in input data cause models to make incorrect predictions. Security reviews help implement defences against such attacks.
- Prevent data poisoning: Malicious actors may inject incorrect or biased data into training datasets, compromising model performance. Threat modelling helps detect and prevent data poisoning attempts.
- Ensure compliance: Many industries have security regulations that AI systems must adhere to. Regular reviews ensure compliance with standards like NIST, ISO, and SOC 2.
Implementing This Best Practice
- Use AI-specific threat modelling frameworks: Leverage frameworks like MITRE ATLAS to assess adversarial threat models specific to AI systems. These frameworks help map out potential threats and guide mitigation strategies.
- Conduct regular security audits: Perform audits to assess the security of data, models, and infrastructure. Tools like OWASP can be used to check for vulnerabilities in web services that expose AI models.
- Implement defences against adversarial attacks: Use techniques like adversarial training (training models on adversarial examples) or defensive distillation (making the model more resilient to adversarial inputs) to reduce vulnerability to attacks.
- Secure the AI pipeline: Ensure that the entire pipeline, from data ingestion to model deployment, is secure. Use encryption, secure APIs, and access control mechanisms to protect each component of the pipeline.
Conclusion
Regular security reviews and threat modelling are essential for safeguarding AI systems against adversarial threats and data breaches. By using specialised frameworks like MITRE ATLAS and conducting thorough security audits, organisations can mitigate risks and ensure the integrity of their AI pipelines. These practices are critical to building secure, trustworthy AI systems.
