Artificial intelligence (AI) and machine learning (ML) are valuable tools with wide-reaching applications. As AI becomes more advanced, it will increasingly become a core part of the security landscape. AI has both offensive and defensive applications, used to develop new types of attacks and create defenses against them.
AI is already used in security, and its role will continue to grow over time. Some of the benefits of AI for security include the following:
AI is a useful tool, but it isn’t perfect. Some of the challenges of implementing AI in security include the following:
AI has numerous potential applications in security. Some example use cases include:
AI is a powerful tool, but it can also be a dangerous one if used incorrectly. When designing and implementing AI-based solutions for security, it is important to consider the following best practices.
AI is a promising tool for security. It is ideally suited to solving many of the main challenges that security teams face, including large data volumes, limited resources, and the need to respond rapidly to cyberattacks.
However, AI is not a magic bullet and must be strategically integrated into an organization’s security architecture to be effective. A key part of using AI for security is identifying how AI can be best deployed to address an organization’s security challenges and developing a strategy for integrating AI into an organization’s security architecture and processes.
AI is only as good as the data used to train and operate it. An organization can enhance the effectiveness of an AI system by providing it with more, higher-quality data to provide a more contextual, complete view of an organization’s security posture.
However, AI’s data usage can create concerns. If the data is corrupted or incorrect, then the AI system will make incorrect decisions. Sensitive data provided to the AI system may be at risk of exposure. When developing an AI strategy, an organization should consider how it will ensure data quality and privacy when operating its AI system.
AI is a “black box” that operates using a model whose quality is dependent on the quality of the data used to train it. If that data is biased or unfair, the AI model will be as well.
AI systems can enhance security operations, but it is important to consider and address the ethical implications of their use. For example, if bias in an AI system could negatively affect an organization’s employees, customers, vendors, etc., then the AI system should not be used as the final authority when making those decisions.
The quality of an AI system’s model depends on the data used to train it. If that data is incomplete, biased, or out-of-date, then the AI system may not make the best decisions.
An organization using AI systems should periodically test and update their models to ensure that they are up-to-date and correct. This is especially true when using AI for security since the rapidly-evolving security landscape means that older AI models may be incapable of detecting newer attacks.
There’s no doubt that AI’s role in cyber security will only grow over time. Here are three predictions for how AI’s role in security will evolve:
AI and machine learning have received a great deal of attention in recent years, but the technology is in its infancy. As AI and machine learning technologies improve and advance, their utility and potential security applications will only increase.
AI is emerging and evolving in parallel with other technologies, such as 5G mobile networks and the Internet of Things (IoT). The integration of these emerging technologies has promising implications for security, combining IoT’s data collection and remote management capabilities with AI’s decision-making abilities.
Like many other industries, AI will have an impact on the security industry and job market. As AI is used to perform repetitive tasks and enhance security operations, human operator roles will increasingly focus on partnering with these systems to provide enhanced security at scale.
Check Point solutions already integrate AI to enhance their threat prevention capabilities. One example of how Check Point uses AI is Check Point Horizon XDR/XPR. To learn more about Horizon XDR/XPR and its use of AI for security, sign up for the Early Availability Program.