Page 116 - Cyber Defense eMagazine September 2025

AI doesn’t just detect threats; it learns from them in real-time

Despite upfront investment, AI driven cybersecurity often reduces long term breach cost, labor hours and
penalties. IBM’s 2024 Cost of a Data Breach Report found that the average cost of data breach reached
$4.4 million in 2024 and a key finding was that use of AI and automation, on average, reduced breach
costs by $2.2 million. AI can analyze millions of datasets in real time and can detect anomalies faster. It
can operate round the clock and continuously scan for threats across devices, networks and cloud
services. Machine learning modules can detect subtle behavioral shifts or unseen attack patterns that
traditional tools might miss. In short, AI augments cybersecurity teams by providing greater coverage,
speed and context.

In a world where identity is the new perimeter, AI is the gatekeeper

Five critical areas where AI delivers the most impact are threat detection and anomaly monitoring, Identity
Access Management (IAM), malware detection, user behavior analytics (UBA) and automated response.
AI-powered systems monitor network traffic and system behavior to detect anomalies that may be an
indication of malicious activity such a sudden spike in outbound traffic from a server during off hours, a
user attempting unauthorized access and unusual login patterns. By incorporating ML, these systems
continuously adapt to threats and anomalies to reduce false positives. Furthermore, in a world where
hybrid and remote work is becoming more common, identity is the new perimeter in cybersecurity.
According to IBM X-Force 2025 Threat Intelligence Index, identity-based attacks make up 30% of total
intrusions. AI is reshaping IAM through adaptive authentication and biometric verification, with large
language models (LLMs) capable of continuously learning user behavior and dynamically adjusting
authentication and authorization protocols in real time. Traditional antivirus depends on predefined
signatures to identify known threats. On the other hand, AI driven malware detection uses behavioral
analysis to detect suspicious activity. As per Watchguard Threat Lab Q2 2024, 46% of malware deployed
techniques that were specifically designed to evade signature-based tools. Additionally, AI enables the
implementation of UBA which tracks how individuals typically behave across systems and then flags
anomalies that indicates compromised accounts or insider threats. Finally, AI also powers Security
Orchestration, Automation and Response (SOAR) tools. By reducing mean time to detect (MTTD) and
mean time to respond (MTTR), AI driven cybersecurity minimizes damage and lowers operational burden
on cybersecurity experts.

Cyber Defense eMagazine – September 2025 Edition 116
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