Friend or Foe? AI's Complicated Role in Cybersecurity

Staying informed about the latest AI security solutions and best practices is critical in remaining a step ahead of increasingly clever cyberattacks.

Dilip Bachwani, Chief Technology Officer, Qualys

July 3, 2024

4 Min Read
Source: Alexey Kotelnikov via Alamy Stock Photo


The mad dash to the cloud a few years back left many organizations scrambling to understand the true implications of this technological shift. Fueled by promises of scalability and cost savings, many companies jumped on board without fully comprehending key details. For example, many were asking how secure their data was in the cloud, who was responsible for managing their cloud infrastructure, and if they would need to hire new IT staff with specialized cloud expertise. Despite these unknowns, they forged ahead, lured by the possibilities. In some cases, the risks paid off, whereas in other situations, it added a whole new set of headaches to solve.

Today, we see a similar phenomenon emerging with artificial intelligence (AI). Feeling pressured to join the AI revolution, companies often are rushing to implement AI solutions without a clear plan or understanding of the associated risks in doing so. In fact, a recent report found that 45% of organizations experienced unintended data exposures during AI implementation.

With AI, organizations often are so eager to reap the benefits that they overlook crucial steps, such as conducting thorough risk assessments or developing clear guidelines for responsible AI use. These steps are essential to ensure AI is implemented effectively and ethically, ultimately strengthening, not weakening, an organization's overall security posture.

The Pitfalls of Haphazard AI Use

While threat actors are undoubtedly wielding AI as a weapon, a more insidious threat lies in the potential misuse of AI by organizations themselves. Rushing into AI implementation without proper planning can introduce significant security vulnerabilities. For example, AI algorithms trained on biased datasets can perpetuate existing social prejudices, leading to discriminatory security practices. Imagine an AI system filtering loan applications that unconsciously favors certain demographics based on historical biases in its training data. This could have serious consequences and raise ethical concerns. Furthermore, AI systems can collect and analyze vast amounts of data, raising concerns about privacy violations if proper safeguards aren't in place. For instance, an AI system used for facial recognition in public spaces, without proper regulations, could lead to mass surveillance and loss of individual privacy.

Enhancing Defenses With AI: Seeing What Attackers See

While poorly planned AI development can create security vulnerabilities, proper AI due diligence can open a world of opportunity in the fight against threat actors. For the strongest defenses, the future lies in the ability to adopt the perspective of attackers, who will continue to rely more heavily on AI. If you can see what attackers see, it's much easier to defend against them. By analyzing internal data alongside external threat intelligence, AI can essentially map out our digital landscape from an attacker's point of view, highlighting critical assets that are most at risk. Given all the assets that need to be protected today, being able to zero in on the ones that are most vulnerable and potentially most damaging is a huge advantage from a timing and resources standpoint. 

Furthermore, AI systems can mimic the wide range of tactics of an attacker, relentlessly probing your network for new or unknown weaknesses. This consistent and proactive approach allows you to prioritize security resources and patch vulnerabilities before they can be exploited. AI can also analyze network activity in real-time, enabling faster detection and response to potential threats.

AI Is Not a Silver Bullet

It's also important to acknowledge that AI in cybersecurity — even when it's implemented the right way — is not a silver bullet. Integrating AI tools with existing security measures and human expertise is crucial for a robust defense. AI excels at identifying patterns and automating tasks, freeing up security personnel to focus on higher-level analysis and decision-making. At the same time, security analysts should be trained on interpreting AI alerts and understanding their limitations. For instance, AI can flag unusual network activity, but a human analyst should be the last line of defense, determining if it's a malicious attack or a benign anomaly.

Looking Ahead

The potential for AI to truly revolutionize cybersecurity defenses is undeniable, but it's important that you know what you're signing up for before you dive in. By implementing AI responsibly and adopting a proactive and intelligent approach that takes an attacker's perspective into account, organizations can gain a significant advantage in the ever-evolving battle against cyber-risk. However, a balanced approach with human intervention is also key. AI should be seen as a powerful tool to complement and enhance human expertise, not a silver bullet that replaces the need for a comprehensive cybersecurity strategy. As we move forward, staying informed about the latest AI security solutions and best practices will be critical in remaining a step ahead of increasingly clever cyberattacks.

About the Author(s)

Dilip Bachwani

Chief Technology Officer, Qualys

As the chief technology officer of Qualys and executive vice president of the Enterprise TruRisk Platform, Dilip Bachwani is responsible for leading global product development, data and platform engineering, DevOps, site reliability engineering, cloud operations, and customer support across Qualys’s broad security product portfolio. Dilip joined Qualys in 2016 to drive Qualys’s own internal digital transformation efforts and has been instrumental in helping scale the technology and organization in support of the company’s accelerated product growth and transformation into a unified security platform. Prior to joining Qualys, Dilip served in multiple engineering leadership roles at various mid-sized and large organizations to build and deliver complex, scalable, distributed enterprise SaaS products and big data cloud platforms. Dilip has a bachelor’s degree in electronics engineering from the University of Mumbai and a master’s degree in computer science from Ball State University.

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