A New Era of Protection: How AI is Transforming Security Controls

As cyber threats evolve, traditional security controls at times struggle to keep up with the speed of maintaining security and protecting against attacks. Then, artificial intelligence (AI) comes along! AI is a buzzword with far-reaching ramifications, both good and bad. While AI offers potential to increase and enhance security controls such as threat detection, automated incident response and risk management, it also introduces challenges and risks. We explore some of these challenges and outline the top three security controls to consider when leveraging AI.

Challenges in AI: Bias in Models

One area of concern is focused on bias in AI models. These biases can lead to false positives or missed threats, impacting the reliability of the output. Bias in AI models occurs based on how the models are trained. For example, if an AI model is trained to only recognize distributed denial of service (DDoS) attacks as the way a malicious actor uses to comprise a system, it may overlook other attack vectors, such as a structured query language (SQL) injection. This could impact an organization’s services and/or reputation.

Risks of Adversarial Attacks on AI Models

Adversarial attacks pose another risk, where cybercriminals could manipulate AI models to bypass detection, making security breaches harder to prevent. By exploiting weaknesses in the AI’s decision-making process, attackers can confuse the system by presenting inputs that fall outside its parameters. This is due to the dependency AI models have on large datasets to make decisions about how to react to inputs, which could cause a failure in detecting attacks that deviate from what the model has been trained to recognize. Solely relying on AI to detect and prevent intrusion is working on a false assumption.

The complexity of AI models means it can be difficult for security teams to interpret how decisions are made, potentially slowing down incident response and troubleshooting. It’s essential to have security controls in place to mitigate such attacks and ensure protection.

Top Three Security Controls to Safeguard AI Models

Below are the top three security controls to consider when leveraging an AI model:

1. Access and authentication: As with any application used today, organizations need to ensure they have the appropriate restrictions in place for access. Lack of appropriate permissions and authentication can allow hackers to access AI systems and distort their models.

2. Data integrity and privacy protection: When leveraging an AI tool, organizations are encouraged to use caution to restrict data access, as well as carefully restricting what the AI is allowed to return. Currently, users may view AI as a voice of authority. This can lead to incorrect beliefs if the AI system returns data that is factually or subjectively inaccurate.

3. Continuous monitoring: Keeping logs of interactions with your AI can help you watch for attempts to poison your models with bad data, as well as attempts to extract restricted data items. These logs support accountability, strengthen oversight and help teams respond quickly to unusual or unauthorized activity.

Whether leveraging AI internally within an organization’s infrastructure or using it as an outsourcing tool, it’s essential for organizations to keep security top of mind to protect their company’s assets. For additional controls and governance checklists, a great starting point is the Open Worldwide Application Security Project’s (OWASP’s) Top 10 for LLM Applications and the LLM AI Cybersecurity and Governance Checklist.

Contact us — we can help you navigate these resources and provide maturity assessments, readiness evaluations or examinations of your AI model to help your organization stay informed and prepared in this evolving landscape.

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