Cyberattacks evolve every moment, fueled by constant breakthroughs in artificial intelligence (AI). Tools that were once only available to sophisticated attackers are becoming increasingly more accessible, enabling adversaries to craft attacks with alarming speed, scale, and personalization.

It’s a new reality that demands a proactive, intelligent approach to defense. So, how can organizations understand their resilience against adversaries wielding advanced AI capabilities?

In this landscape, red teaming in AI is emerging as crucial. It goes beyond traditional security testing by involving ethical hackers simulating real-time attacks specifically leveraging generative AI and other machine learning techniques.

Let’s explore red teaming, how it’s adapting to AI, and its vital role in combating AI-powered social engineering.

What is Red Teaming in AI? More Than Just Hacking

Red teaming involves ethical hackers stepping into the shoes of their adversaries, meticulously emulating the tactics, techniques, and produces (TTPs) that attackers use in the wild.

Unlike penetration testing, which focuses on finding specific technical vulnerabilities, red teaming takes a holistic, objective-driven approach.

Organizations that conduct red teaming understand the goal isn’t just to breach a system; it might be to access specific sensitive data, disrupt operations, or test the response capabilities of the organization’s defenders (the ‘blue team’) — all while trying to remain undetected.

Red teaming assesses the entire security posture: technology, processes, and people. The result is actionable insights into how well an organization can withstand and respond to a targeted attack, revealing critical gaps that need to be addressed.

From Military Strategy to Cyber Necessity: The Evolution of Red Teaming

Red teaming isn’t new, and its roots actually lie in military strategy, where exercises were used to challenge assumptions and stress-test plans. Over time, the private sector, especially the finance and other critical infrastructure industries, adopted the practice to proactively find vulnerabilities, initially focusing on physical and network security.

As cyberattacks increased, red teaming shifted toward the digital landscape. Its adoption has surged ever since, with an Exabeam survey noting that 92% of companies reported conducting red teaming exercises in 2020, up from 72% in 2019.

While technical exploits remain as relevant as ever, today’s exercises place significant emphasis on exploiting the human element through:

• Open-Source Intelligence (OSINT): Gathering publicly available information, from sources such as social media and company websites, about the organization and its employees.
• Social Engineering: Using gathered intelligence to manipulate employees into revealing credentials, downloading malware, or granting access, such as through highly personalized phishing.
• Credential Harvesting: Specifically targeting login details, often referred to as the ‘keys to the kingdom.’

It’s an evolution that underscores the modern reality where, despite technological defenses, humans often represent the most targeted and exploitable vulnerability in the security chain.

Testing the Defenses: People, Processes, and Technology

Simulating human-focused attacks reveals how easily technical defenses can be bypassed through clever manipulation. A red team might use OSINT to craft a convincing spear-phishing email targeting a specific executive, demonstrating a realistic path an attacker could take.

Concurrently, red teams continue to probe technical infrastructure — testing firewalls, intrusion detection systems, endpoint security, and patch management. They simulate lateral movement within networks, attempt privilege escalation, and test data exfiltration routes, mimicking an attacker’s post-breach actions.

To overcome the limitations of point-in-time assessments, organizations embrace continuous automated red teaming (CART). These automated tools constantly simulate adversary TTPs, providing ongoing feedback on defensive capabilities and response times.

The New Frontier: Red Teaming vs AI-Driven Threats

Integration of AI into cyberattacks represents a significant escalation, and this is where red teaming in AI demonstrates unique value. It’s important to note that red teaming in AI can encompass several aspects, including testing the security and biases of AI models themselves. 

However, a primary focus for many organizations today is red teaming against threats supercharged by AI, particularly AI-powered social engineering:

• Hyper-Personalized Phishing: AI crafts incredibly convincing emails, messages, or smishing (SMS phishing) text messages at scale, tailored to individual targets based on scraped data.
• Deepfakes: AI generates realistic voice clones of executives or creates fake video representations, making urgent requests or authorizing fraudulent actions seem legitimate.
• Alterable Malware: AI creates malware that changes behavior to evade detection.

Initiatives like Project Naptime demonstrate this duality: AI enhances defenses, but it’s also weaponized to create more effective attacks that blur the line between real and fake, making human detection significantly harder.

Red Teaming AI Threats to Inform Advanced Training

To effectively counter these emerging threats, red teaming in AI requires adapting TTPs. Red teams must now incorporate simulations of sophisticated, AI-enhanced attacks.

The findings from simulations directly highlight vulnerabilities, particularly the human element’s susceptibility to convincing AI-generated phishing attacks. Observing how employees react to these realistic AI-driven scenarios provides invaluable data that underscores the need for evolved security awareness training.

Therefore, the insights from red teaming in AI exercises should directly inform the content and structure of security awareness training programs. Effective, modern training must:

• Utilize Realistic AI Simulations: Incorporate training modules that use AI-generated phishing examples, deepfakes, or scenarios mirroring the sophistication employees now face.
• Implement Adaptive Learning: Leverage AI within the training platform itself to analyze employee performance and tailor subsequent exercises to address weaknesses identifies during red team simulations or training interactions.
• Focus Explicitly on Recognition: Go beyond generic phishing guidance and actively teach employees the nuances of spotting AI manipulation, including potential tells in deepfakes or hyper-personalized scams.

By directly linking the simulated threats tested by the red team to the training provided, organizations create a much more effective feedback loop, continuously strengthening their human firewall against the specific AI-powered attacks they’re likely to encounter.

Staying Ahead with Red Teaming in AI

Red teaming has evolved into an indispensable cybersecurity practice.

As artificial intelligence continues to shape the threat landscape at an accelerating pace, red teaming in AI is no longer merely beneficial. Specifically focusing on simulating and defending against AI-enhanced attacks like social engineering, this practice is now essential for an organization.

How can companies stay ahead? By embracing advanced red teaming techniques tailored specifically to the AI era and investing in AI-centric security awareness training that’s directly informed by the real world. This combination allows organizations to significantly strengthen their overall resilience.

Adaptive Security offers the next-generation platform needed to effectively prepare employees for the nuances and dangers of the evolving threat landscape.

Ultimately, investing strategically in both rigorous red teaming in AI and a platform complete with security training and phishing simulations is critical. It’s the necessary path forward for building robust, future-proof defenses against sophisticated cyberattacks that are growing more sophisticated by the day.

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