Artificial intelligence (AI) has taken the place cybersecurity The cybersecurity industry is booming, with vendors of all kinds racing to integrate AI into their solutions. But the relationship between AI and security is about more than just implementing AI capabilities: it’s about how attackers and defenders leverage the technology to change the face of the modern threat landscape. It’s also about how those AI models are developed, updated, and protected. Today, AI sits on three main pillars in cybersecurity. And as more organizations turn to security vendors that offer AI-powered solutions, it’s increasingly important to understand how the technology is actually being used.
Pillar #1: Defend AI Capabilities
As adoption of AI-powered solutions continues to skyrocket, organizations are increasingly recognizing that protecting these solutions must be a priority. AI solutions are trained on massive amounts of data (the more data, the more accurate the solution), which means that an attacker who successfully breaches one of these solutions could be sitting on a treasure trove of customer data, intellectual property, financial information, and other valuable assets. With attackers exploiting these attack vectors at an increasing rate, organizations’ first line of defense is their ability to defend the AI models they use every day.
Fortunately, this problem is not a secret: in fact, the market for solutions specifically designed to protect AI models is growing rapidly, with a significant number of startups emerging in the last couple of years. It’s also important to remember that while solutions like generative AI are relatively new, AI has been around for a while, and most AI solutions have some degree of security. integrated into themThat said, organizations should still take whatever extra steps are necessary to protect themselves and their data, and there’s no shortage of third-party solutions that can help defend AI pipelines from attackers looking for an easy score.
Pillar #2: Stopping attackers using AI
As AI becomes more accessible, it’s no surprise that attackers are leveraging the technology for their own purposes. Just as AI allows businesses to streamline operations and automate tedious, repetitive processes, it also helps attackers increase the scale and complexity of their attacks. In practical terms, attackers aren’t really using AI to conduct “new” types of attacks—at least not yet. But the technology does make it easier to implement existing attack tactics at extremely high volume.
For example, phishing scams are a numbers game: if just 1% of recipients click on a malicious link, it’s a win for the attacker. with the help of AIAttackers can apply an unprecedented level of personalization to their phishing emails, making them more convincing and dangerous than ever before. Worse yet, once an organization has been compromised (via phishing or other means), the attacker can leverage AI to analyze the discovery data and create a decision-making process that makes propagation both easier and stealthier. The more attackers can automate propagation, the faster they can achieve their goal—often before traditional security tools can even identify the attack, let alone respond effectively.
This means businesses need to be prepared, and that starts with implementing solutions that can identify and defend against these high-volume, high-complexity attacks. While many businesses have solutions in place to defend against phishing scams, malware attacks, and other vectors, it’s important to test those solutions to ensure they remain effective as attacks become more frequent and complex. Security leaders must remember that it’s not just about implementing the right solutions, but also about ensuring they work as intended against real-world threats.
Pillar #3: Use of AI in cybersecurity products
The final pillar is the one that security professionals are most familiar with: cybersecurity vendors using AI in their products. One of AI’s most important functions is pattern recognition, making it ideal for identifying suspicious or anomalous activity. A growing number of vendors are deploying AI in their detection solutions, and many are also using it to automate some elements of remediation. In the past, managing low-level threats was a tedious but necessary part of cybersecurity. Today, AI can automate much of this process, automatically addressing minor incidents and allowing security professionals to focus only on threats that require direct attention.
This evolution has dramatically improved the value of a wide range of security solutions, but it doesn’t happen in a vacuum. AI models need to be maintained, and it’s important to work with vendors that have a reputation for keeping their models up to date at all times. Vet potential security partners are critical, and organizations should understand how vendors work with AI: where their data comes from, how they avoid issues like inherent bias, and other factors that can (and should) influence the decision to work with a particular vendor. While AI solutions are gaining traction in nearly every industry, they’re not all created equal. Organizations need to ensure they’re working with security partners who understand the ins and outs of the technology, rather than vendors who see “AI” as just a marketing buzzword.
Approaching AI with Confidence
As AI becomes more pervasive in the cybersecurity landscape, it’s important for organizations to familiarize themselves with the ways in which this technology is actually being used. This means understanding both how AI can enhance security solutions and how it can help attackers design more advanced attacks. It also means recognizing that the data that underpins current AI models must be protected, and it’s critical to work with vendors who prioritize deploying the technology securely. By understanding the three main pillars of AI and security, organizations can ensure they have the foundational knowledge they need to approach the technology with confidence.
