Top 10 security risks auditors should focus on when performing an audit over Artificial Intelligence (AI) Systems:
- Data breaches and unauthorized access: AI systems rely heavily on vast amounts of data. It is crucial to ensure that adequate security measures are in place to protect the data from breaches, such as unauthorized access, hacking, or insider threats.
- Algorithmic bias and fairness: AI algorithms can be biased and discriminatory if they are trained on biased datasets or programmed with biased rules.
- Adversarial attacks: AI models can be vulnerable to adversarial attacks, where malicious actors intentionally manipulate the input data to deceive or mislead the AI system. Auditors should evaluate if appropriate safeguards are implemented to identify and prevent such attacks.
- Model integrity and accuracy: AI models can degrade over time due to changing data patterns or external factors. Auditors should assess the mechanisms set in place to continuously monitor and evaluate the model’s integrity and accuracy, ensuring it aligns with defined expectations.
- Privacy risks: AI systems often handle sensitive personal or corporate data. Auditors need to evaluate the privacy policies and controls implemented to protect the privacy rights of individuals whose data is processed by the AI system, complying with relevant privacy regulations and standards.
- Compliance and legal considerations: Auditors should ensure that AI systems comply with applicable laws, regulations, and standards. This includes assessing if they meet specific industry requirements and legal obligations, such as GDPR, HIPAA, or financial regulations.
- Vulnerability to manipulation and deception: Auditors should assess if AI systems incorporate robust measures to detect and prevent manipulation, ensuring the reliability of the outcomes.
- Lack of transparency: AI models, particularly complex ones like deep learning, can be opaque and challenging to interpret. Auditors should evaluate if the AI system provides sufficient explanations for its decision-making processes, facilitating transparency and understandability.
- Supply chain risks: Auditors need to analyze the entire AI supply chain, including the data sources, model development, and deployment phase. Assessing the security controls at each stage is essential to prevent security vulnerabilities from being introduced and propagated throughout the AI lifecycle.
- Continuity and recovery: Auditors should review the business continuity and disaster recovery plans for AI systems to ensure they can recover from unforeseen events, such as system failures, natural disasters, or cyberattacks, while minimizing the impact on operations.
By focusing on these security risks, IT auditors can help organizations identify vulnerabilities, mitigate risks, and enhance the overall reliability and trustworthiness of AI systems.
