Investing in AI as a cybersecurity capability is not only necessary but also needed. The cybersecurity landscape is constantly evolving, and threats are becoming more sophisticated every day. Adversaries are leveraging AI and machine learning to develop new attack vectors and evade traditional security measures. It is also important to note that with AI becoming more widely available, the barriers to entry for cybercriminals lowers and the ability to utilize AI for attack development and execution grows. This makes it imperative for organizations to start investing in AI to proactively protect themselves and stay ahead of the threat landscape. In addition, investing in AI can allow organizations to better understand their vulnerabilities and potential attack scenarios enabling them to proactively mitigate risks and improve their overall security posture.
As a result, AI red teaming is becoming an important and necessary capability for enterprises today. The use of AI can enhance the capabilities of red teams, enabling them to enhance the simulation of real-world attacks and identify weaknesses in an organization's defenses. AI is already being used by companies to develop offensive tools that address specific use cases and improve the effectiveness of red team engagements. As a penetration tester and red teamer, I actively utilize AI to help generate phishing emails/develop social engineering campaign stories, gather and aggregate information about targets, and increase malicious code development.
One key area where AI can make a significant impact is the area of code obfuscation. Red teamers often employ obfuscation techniques to hide the true intent and functionality of malicious code, making it harder for defensive security systems to detect and respond to attacks. By leveraging AI, red teamers can automate the process of modifying their code to include obfuscation techniques such as encryption or polymorphism. This allows them to adapt their attack techniques to evade detection by security systems and more effectively test an organization's defenses. However, it is important to note that AI is not a silver bullet solution and should be used in conjunction with other security measures to ensure comprehensive protection against cyber threats.
For enterprises that incorporate artificial intelligence (AI) and machine learning (ML)-based systems into their everyday operations, the first steps they can take to maintaining a comprehensive understanding of their security posture include:
- Conduct a comprehensive security assessment to help identify vulnerabilities in their system and provide a baseline for measuring the effectiveness of security controls. This should include vulnerability scanning, penetration testing and code review.
- Establish and continuously review security controls for the AI/ML-based system. Security controls should include access control measures, authentication mechanisms and data protection measures.
- Establish and perform threat modeling to identify potential attack scenarios and prioritize security measures based on likelihood and impact.
- Implement monitoring and detection to assist with identifying and responding to potential threats.
It is important to note that although these are general first steps, they should be tailored to the specific needs of the enterprise and their AI/ML-based systems.
Building an AI-based red teaming platform will require experts in artificial intelligence and machine learning who can develop and train red teaming AI machine learning models, natural language processing models and neural networks. Organizations will need:
- Red teamers/penetration testers: To build an effective AI-based red teaming platform, it is essential to have offensive cybersecurity experts who understand the current threat landscape, the latest attack techniques and the vulnerabilities that adversaries may exploit. Red teamers and penetration testers will help develop realistic attack scenarios and validate the effectiveness of the AI system.
- Software engineers/developers: To develop the AI-based red teaming platform, a team of skilled software developers will be required to build, test, and deploy the platform. These individuals should have experience with developing and integrating AI-based tools and frameworks in existing platforms.
- Data scientists: Lastly, the success of an AI-based red teaming platform will highly depend on the quality and relevance of the data used to train the machine learning models. Data scientists will assist developers to ensure that the data is accurate, unbiased and representative of real-world scenarios.