AI-Based Red Teaming: Why Enterprises Need to Practice Now

Solutions Review’s Contributed Content Series is a collection of contributed articles written by thought leaders in enterprise software categories. Brette Geary of Camelot Secure urges enterprises to incorporate AI-based red teaming into their strategy now.

In the dynamically changing world of cybersecurity, no one can afford to become complacent. As tech professionals, we understand this constant flux, watching as adversarial actors continually adapt their strategies and tools. We are stepping into an era where artificial intelligence (AI) is no longer a futuristic concept, but an active player in the cybersecurity landscape. This article aims to shed light on a significant aspect of AI in cybersecurity– its role in red team exercises.

In this article, we’ll explore why investing and integrating AI into red team operations should be a crucial part of any cybersecurity company’s strategic radar, and how this integration can help organizations stay one step ahead of ever-evolving threats.

AI-Based Red Teaming: Why Enterprises Need to Practice Now

The Urgency of AI-Based Red Teaming

Integrating AI into cybersecurity capabilities is not just a forward-thinking approach; it’s an urgent requirement. The cybersecurity landscape is constantly evolving, and threats are becoming more sophisticated every day. AI and machine learning are not just buzzwords but tools increasingly being weaponized by adversaries to develop new attack vectors and evade traditional security measures. The democratization of AI brings numerous advantages but also lowers the barriers of entry for cyber-criminals, enabling them to harness AI for the development of exploits and execution of attacks. The rapid evolution of cyber threats requires organizations to go beyond reactive responses and proactively invest in AI to shield themselves effectively. Moreover, investing in AI empowers organizations to gain a deeper understanding of their vulnerabilities and anticipate potential attack scenarios, enabling proactive risk mitigation and enhancing overall security posture.

Against this backdrop, the integration of AI into red teaming operations is quickly transforming into a vital capability for contemporary enterprises. AI, when leveraged appropriately, can considerably enhance the abilities of red teams. It enables them to simulate real-world attacks more convincingly, thereby unearthing weaknesses in an organization’s defense mechanisms. Many organizations are already harnessing AI to develop innovative offensive tools to address specific use cases and bolster the effectiveness of red team engagements. With a background in penetration testing and red teaming, I can testify to the power of AI in aiding the generation of phishing emails and the development of social engineering campaign narratives, the gathering and aggregation of target information, and the significant amplification of malicious code development capabilities.

In the offensive domain, one area where AI proves to be a game-changer is code obfuscation. Obfuscation is a technique used by red teamers, and adversaries alike, to mask the true intent and functionality of malicious code. Red teamers frequently resort to obfuscation to make their simulated attacks stealthier and challenge a defensive security system’s ability to detect and counteract these threats. By integrating AI into this tactic, red teamers can automate, enhance, and accelerates the process of incorporating obfuscation techniques into their code. These techniques include aspects like encryption, which disguises the data within the code, and polymorphism, where the code changes each time it runs but maintains its original functionality. AI-assisted automation not only quickens this process but also allows the red teamers to constantly adapt their attack strategies to evade detection by security systems. The result? An enhanced capability to pressure-test an organization’s defenses effectively.

However, while AI’s role is transformative, it is vital to remember that it is not a panacea for all cybersecurity issues. It should be employed as a part of a wider security strategy. AI enhances our ability to respond to cyber threats, but it should work in harmony with other security measures to provide a comprehensive and multi-layered defense against cyber threats. Remember, a diverse defensive portfolio is key to a robust security stance.

Maintaining Security Posture

As we delve deeper into the potent combination of AI and red teaming, it’s clear that organizations need to approach this with a comprehensive plan. For enterprises that already incorporate AI and machine learning (ML) systems into their daily operations, the shift to an AI-enhanced red teaming approach can be an organized progression rather than a sudden leap. Let’s explore a systematic approach that these organizations can adopt to maintain a thorough understanding of their security posture:

1. Conduct a comprehensive security assessment: This forms the foundation of your security strategy. Identify vulnerabilities in your system and create a benchmark to measure the effectiveness of your security controls. Techniques like vulnerability scanning, penetration testing, and code review should be employed for a thorough assessment.
2. Establish and routinely review security controls: These controls are specific to your AI/ML-based systems. They should include a variety of access control measures, robust authentication mechanisms, and effective data protection measures.
3. Implement threat modeling: This practice will help identify potential attack scenarios. Once identified, security measures can be prioritized based on the likelihood of occurrence and the potential impact.
4. Integrate monitoring and detection mechanisms: A proactive security stance also includes real-time identification and response to potential threats. Monitoring and detection mechanisms help in achieving this.

Bear in mind that while these steps provide a general pathway, the specifics must be customized to the unique needs of your enterprise and the nature of your AI/ML-based systems. The multi-faceted nature of AI and ML presents a challenging but exciting undertaking for organizations. This broad scope inevitably leads us to discuss a fundamental aspect of integrating AI into red teaming: the people who make it happen.

Building an AI-Based Red Team

Building an AI-based red teaming platform calls for a unique blend of skills and expertise. It’s not just about having proficiency in AI and ML, but also about understanding the interplay between technology, security, and the threat landscape.

So, who are the players in this team?

• Red Teamers/Penetration Testers: The linchpins of any successful red teaming operation, these cybersecurity experts bring a deep understanding of the current threat landscape, the latest attack techniques, and the vulnerabilities that adversaries may exploit. They provide the backbone for realistic attack scenarios and are crucial for validating the effectiveness of the AI system.
• Software Engineers/Developers: Responsible for developing the AI-based red teaming platform, a team of skilled software developers enables the building, testing, and deploying of such platforms. Their experience with integrating AI-based tools and frameworks into existing systems is invaluable.
• 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.

As we navigate through an increasingly complex cyber threat landscape, AI’s role in red teaming emerges as an essential consideration for all tech professionals, especially CISOs. We’ve explored the transformative power of AI and how it can be harnessed to enhance red team operations, particularly in areas like code obfuscation. We’ve also highlighted the vital steps for organizations using AI/ML-based systems to bolster their cybersecurity postures, underlining the necessity of tailoring the approach to their unique needs. And we’ve delved into the people who play pivotal roles in creating an effective AI-based red teaming platform– from red teamers and software developers to data scientists. Each player brings their expertise to the table, contributing to the development of a robust platform capable of keeping organizations one step ahead of the evolving threats. As we continue to embrace the potential of AI in cybersecurity, the integration of AI into red teaming will undoubtedly become a crucial element in our collective journey toward building more secure digital landscapes.

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Brette Geary

Brette Geary is the Offensive Security Lead at Camelot Secure. Brette initially attended the United States Naval Academy (USNA) to study engineering and pursue a career as an Officer, when she pivoted to a Cyber Operations major that piqued her interest. While there, Brette was a member of the USNA’s Women’s Rugby Team, a participant in the USNA Cyber Lectures with USCYBERCOM, NSA, and DHS, and represented USNA at the Women in Cyber Security Conference. With her time cut short at the USNA due to medical issues, Brette entered the workforce as an SOC Analyst for a Blue Team, and eventually pursued her passion for offensive cyber operations serving as Cyber Security Engineer at SratoZen and Security Engineer at Fortalice Solutions.

Latest posts by Brette Geary (see all)

• AI-Based Red Teaming: Why Enterprises Need to Practice Now - August 25, 2023