AI and Cybersecurity: Friend or Foe?

Cyberattacks—intentional efforts by cybercriminals to access, disrupt, or damage digital systems and data without permission—are evolving fast. In 2023 alone, the average cost of a data breach reached $4.45 million globally. Criminals are no longer just guessing passwords or sending suspicious emails—they’re using tools that learn, adapt, and strike smarter than ever.

Enter artificial intelligence or AI. AI, in simple terms, refers to machines that can mimic human thinking—spotting patterns, making decisions, and improving over time. In cybersecurity, AI is used to scan networks, detect threats, and respond in real time. It’s fast, tireless, and most importantly, very effective.

However, this same power is also being used by attackers. AI is already being used to create scams, smarter phishing attacks, and various malware that can adapt to avoid detection. The same tech that protects can also be weaponized.

So here’s the big question: Is AI the ultimate cybersecurity shield—or the smartest weapon in a hacker’s arsenal?

Well, if you want to know more, read on as we explore:

• How AI is reshaping cyber defense strategies

• How cybercriminals are exploiting AI

• Why human oversight still matters

• What the future holds for AI in cybersecurity

At the end of this article, you’ll understand why AI in cybersecurity isn’t black or white—it’s all about who’s holding the controls.

How AI is making cyber defense better

Curious about how AI has improved cybersecurity? Check out some of real–world uses:

Real-time threat detection

One of the biggest advantages of AI is how quickly it can process massive amounts of data. While human analysts might take hours to spot suspicious activity, AI can do it in seconds.

Example: IBM’s QRadar SIEM uses machine learning and behavior analysis to detect unusual activity across networks, devices, and users. It flags threats that break from normal patterns —like someone trying to access sensitive files at odd hours—and sends alerts so teams can respond immediately.

Predictive security models

AI doesn't just react to threats; it can also predict them. It does this by learning what “normal” behavior looks like inside a system, then spotting early signs of problems.

Example: Darktrace, a company that builds AI-based cybersecurity tools, uses machine learning to build a digital fingerprint for every device, user, and system in a network. If someone suddenly downloads large amounts of data or logs in from an unusual location, Darktrace picks it up and warns the security team. This kind of prediction helps stop attacks before they happen.

Automated incident response

When a security breach occurs, AI-driven incident response systems can automatically execute predefined actions to contain and mitigate threats, such as isolating compromised endpoints, blocking suspicious behavior, or applying security patches (small updates that fix bugs in software). This automation reduces the reliance on manual intervention, accelerates response times, and minimizes the overall impact of security incidents.

​​Example: Microsoft's Security Copilot integrates AI agents designed to autonomously handle high-volume security tasks. These agents can triage phishing and data loss alerts, prioritize critical incidents, and monitor for vulnerabilities, thereby enhancing the efficiency of security operations. 

How cybercriminals use AI

As mentioned earlier, cybercriminals are also using AI to enhance the sophistication and effectiveness of their attacks. Key areas of concern include:

AI-powered phishing and deepfakes

Cybercriminals now use AI to launch more targeted and convincing phishing attacks (in the form of fake texts, social media messages, or even video calls). They scan online data to pick specific targets, time their messages for maximum impact, and use deepfakes— highly realistic fake videos—to impersonate trusted people. These make it challenging for individuals and organizations to distinguish between legitimate and malicious communications.​

Example: In a notable case, a finance worker was deceived into transferring $25 million after participating in a video call featuring deepfake representations of colleagues. The attackers used AI-generated videos to impersonate the company's executives, leading the employee to believe the fraudulent request was legitimate.

Evolving malware

AI enables the development of malware that can adapt its behavior to evade detection by traditional security tools. This self-changing malware can alter its code or signature, making it difficult for antivirus programs to identify and neutralize the threat.​

Example: Polymorphic and metamorphic malware are designed to constantly change their code structure, effectively evading signature-based detection methods. These types of malware utilize AI algorithms to autonomously modify their code, presenting a significant challenge to cybersecurity defenses. 

Language models for scams

Large language models (LLMs)—like the kind behind chatbots—are now being misused to write scam emails that sound professional and real. These tools help attackers mimic trusted people, avoid grammar mistakes, and personalize messages to trick users more effectively.

Example: AI-generated phishing attacks have demonstrated a high success rate, with studies showing that 62% of participants were tricked by these sophisticated emails. This success rate is comparable to traditional phishing methods, highlighting the effectiveness of AI in crafting deceptive communications. 

Striking the balance

So if AI can be used for both good and bad, what is it really?

AI in cybersecurity is neither friend nor foe—it's a tool. Think of it as a hammer: in the hands of a builder, it creates; in the hands of an attacker, it destroys.

What makes AI unique is its amplifying effect. It enhances both defensive capabilities and offensive tactics, accelerating the cybersecurity arms race. Defenders can process more data and respond faster, while attackers can create more convincing scams and adaptive malware.

That means that the distinguishing factor isn't the technology itself but the human element behind it. Organizations that implement AI with proper oversight, clear policies, and regular training can harness its protective potential. Those that deploy it hastily or without adequate human supervision risk unexpected vulnerabilities.

The value of AI in cybersecurity ultimately comes down to intention and implementation. The technology itself is neutral; it's the human decisions surrounding its use that determine whether it serves as a shield or becomes a weapon.

Conclusion

As we've seen, AI represents a double-edged sword in cybersecurity. It significantly enhances defensive capabilities through real-time threat detection, predictive security models, and automated responses. However, it simultaneously provides cybercriminals with powerful tools for creating sophisticated phishing schemes, evolving malware, and convincing scams.

Looking ahead, three key developments will shape AI's role in cybersecurity. First, explainable AI is gaining prominence, making security systems more transparent and trustworthy by allowing humans to understand how and why AI makes specific decisions. Second, we're witnessing an ongoing technological competition, where defensive systems and attack methods continuously evolve in response to each other. Third, there's growing recognition that regulation and ethical AI frameworks are essential to guide responsible development and deployment of these technologies.

Ultimately, as mentioned above, AI itself is neither inherently good nor bad for cybersecurity. The technology merely amplifies our capabilities—it's how we choose to use it that determines whether it becomes our strongest defense or our greatest vulnerability.