Beyond Traditional Defences
For years, cybersecurity relied on signature-based detection. Much like a security guard with a list of known troublemakers, these systems could only spot threats they had seen before. This approach is no longer enough. Modern cyberattacks are sophisticated,
often using novel techniques (known as zero-day attacks) that have no predefined signature. Attackers can mimic legitimate users, moving slowly and quietly within a network to avoid triggering alarms. This shift has rendered traditional methods increasingly ineffective, creating a need for a more intelligent and adaptive form of defence. The sheer volume of data and alerts generated in a modern corporate network is also far beyond what human teams can manually analyse, leading to analyst fatigue and missed signals.
How AI Learns 'Normal'
This is where AI, and specifically machine learning, enters the picture. Instead of looking for known threats, AI-powered defence systems focus on learning what is 'normal' for a specific environment. By continuously analysing vast amounts of data—from network traffic and log-in times to application usage and data access patterns—the AI builds a detailed baseline of typical behaviour for every user and device. This process is known as User and Entity Behavior Analytics (UEBA). Once this baseline is established, the AI's job is to spot deviations. These anomalies, or outliers from normal patterns, are often the earliest indicators of a potential threat long before a full-blown breach occurs.
The AI Toolkit in Action
In practice, this means AI can flag a wide range of suspicious activities that a human or a rule-based system might miss. For example, it can detect when an employee’s credentials are used to log in from an unusual location at an odd time. It can identify a server that suddenly starts transferring large amounts of data to an external address. It might also flag when a user account begins accessing sensitive files it has never touched before or tries to escalate its own privileges. These are all behaviours that, on their own, might seem minor, but when correlated by an AI, can paint a clear picture of a compromised account or an insider threat. These AI-driven systems enhance everything from network security to phishing detection and identity management.
The Human-in-the-Loop Imperative
However, AI is not a silver bullet. One of the main challenges is the issue of false positives, where the system flags a benign activity as malicious, leading to unnecessary alerts. If an employee starts working unusual hours to meet a deadline, an AI might mistakenly flag their activity as suspicious. Therefore, human oversight remains crucial. AI is most effective when used to empower human analysts, not replace them. The AI can sift through mountains of data to surface the most critical anomalies, allowing human experts to apply context and make the final determination. Furthermore, AI systems are only as good as the data they are trained on, and they can be vulnerable to new forms of attack, such as 'data poisoning', where attackers intentionally feed the AI misleading information to create blind spots.
