Five AI Cyber Use Cases That Actually Work (and Two That Don't)

The artificial intelligence revolution is in full flow. With the market size for AI in cybersecurity projected to reach a staggering $146 billion by 2032, organisations worldwide are rapidly adopting AI-powered security solutions. But amidst the marketing hype and ambitious vendor claims, which AI applications genuinely deliver measurable security improvements, and which are overpromised technologies that fall short in practice?

Five Promising AI Use Cases in Cybersecurity

1. Malware Detection and Classification

The reality: AI-powered malware detection represents one of the most mature and effective applications of artificial intelligence in cybersecurity. Traditional signature-based systems can struggle when it comes to identifying new vulnerabilities, but machine learning models excel at identifying previously unknown malware variants.

Why it works: AI-powered malware detection can outperform traditional signature-based systems, particularly when identifying previously unknown threats. Deep learning algorithms can analyse file behaviour, code structures, and execution patterns to identify malicious intent without requiring known signatures. This makes them highly effective against zero-day malware and polymorphic threats that evade conventional detection methods.

The effectiveness stems from AI's ability to process vast datasets of malware samples and benign files, learning complex feature representations that human analysts would struggle to identify manually.

Real-world application: Major endpoint protection platforms now use ensemble methods combining static analysis, dynamic behavioural analysis, and neural networks. These systems can detect zero-day malware by identifying subtle deviations from normal file behaviour patterns, even when the malware uses advanced evasion techniques.

2. User and Entity Behaviour Analytics (UEBA)

The reality: UEBA has emerged as a cornerstone of modern threat detection, particularly for identifying insider threats and compromised accounts. By establishing baseline behaviour patterns, AI systems can detect subtle anomalies that indicate potential security incidents.

Why it works: Human behaviour, whilst complex, follows predictable patterns. AI excels at learning these patterns and identifying meaningful deviations. Unlike rule-based systems that generate excessive false positives, machine learning models can distinguish between genuine anomalies and normal variations in user behaviour.

Real-world application: Amazon's AWS GuardDuty analyses various data sources, including AWS CloudTrail logs, VPC Flow Logs, and DNS logs, to detect abnormal behaviour that may indicate a security breach, including unusual spikes in API calls and atypical network traffic patterns.

The most successful UEBA deployments combine multiple data sources (authentication logs, network traffic, application usage, and file access patterns) to create comprehensive behavioural profiles.

3. Automated Incident Response and Orchestration

The reality: AI-driven security orchestration, automation, and response (SOAR) platforms have proven exceptionally effective at handling routine security incidents, freeing analysts to focus on complex threats requiring human expertise.

Why it works: According to IBM's Cost of a Data Breach Report, organisations with extensive security AI and automation identified and contained data breaches 108 days faster on average than organisations without AI tools. Automation excels at executing predefined response workflows, correlating alerts from multiple security tools, and performing initial triage of security incidents.

Organisations implementing AI-driven incident response can see substantial improvements in response times and operational efficiency, though specific metrics vary significantly based on existing infrastructure and implementation approach.

Real-world application: IBM's Threat Detection and Response services demonstrate this effectiveness, claiming AI capabilities handling up to 85% of alerts through automation rather than human intervention, based on analysis of engagements with 340+ clients.

4. Network Traffic Analysis and Anomaly Detection

The reality: AI-powered network monitoring has revolutionised the detection of sophisticated attacks that evade traditional perimeter defences. Machine learning algorithms can identify subtle patterns in network traffic that indicate lateral movement, data exfiltration, or command-and-control communications.

Why it works: With connected devices projected to generate 79 zettabytes of data by 2025, manual analysis becomes impractical. AI systems can process massive volumes of network data in real-time, identifying patterns across multiple network segments and time periods.

Advanced implementations increasingly use sophisticated algorithms to model network relationships and identify unusual communication patterns that traditional statistical methods might overlook.

Real-world application: Advanced persistent threat (APT) detection has benefited significantly from AI models that can identify low-and-slow attacks spanning extended timeframes. These systems detect subtle changes in traffic patterns, unusual communication protocols, and anomalous data flows that indicate sophisticated threat actor activity.

5. Phishing Detection and Email Security

The reality: AI has transformed email security, moving beyond simple keyword filtering to sophisticated content analysis that can identify social engineering attempts with remarkable accuracy.

Why it works: Machine learning-based email analysis has demonstrated high accuracy rates in distinguishing legitimate communications from phishing attempts. Natural language processing models can analyse email content, sender reputation, and contextual factors to identify sophisticated social engineering attacks.

Real-world application: Organisations use AI-driven email security to try and reduce successful phishing attacks reaching end users. Contemporary email security solutions integrate computer vision for analysing embedded images and logos, natural language processing for content analysis, and behavioural analytics for sender verification, creating multi-layered detection capabilities.

Two AI Use Cases That Consistently Disappoint

1. Predictive Threat Intelligence and Attack Forecasting

The promise: AI systems that can predict specific cyber attacks before they occur, providing organisations with advance warning of targeted campaigns.

The reality: Whilst AI can identify trends and general threat patterns, predicting specific attacks with actionable accuracy remains largely unachievable with current technology.

Why it struggles: Cyber attacks involve human adversaries who adapt their tactics in response to defensive measures. The dynamic nature of threat actors makes precise prediction extremely challenging. AI excels at identifying attack patterns, correlating threat intelligence, and assessing risk levels based on observable indicators. However, these capabilities are better framed as risk assessment and trend analysis rather than attack prediction.

2. Fully Autonomous Threat Hunting

The promise: AI systems that independently discover unknown threats without human guidance, automatically investigating complex attack chains and providing complete incident analysis.

The reality: Whilst AI is good at pattern recognition and anomaly detection, autonomous threat hunting requires contextual understanding, creative thinking, and investigative intuition that current AI systems lack.

Why it struggles: AI systems are susceptible to "AI hallucinations," where they may misinterpret information and make decisions based on incomplete or false data, potentially leading to incorrect threat assessments. Complex threat investigation requires understanding business context, attacker motivation, and subtle indicators that aren't captured in training data.

AI provides excellent support tools for threat hunters, but human expertise remains essential for complex investigations. The most successful implementations use AI to surface interesting anomalies whilst relying on skilled analysts for investigation and validation.

Combine the power of AI with human expertise

OSec's expert penetration testing team use AI in a smart, focused way to help identify potential blind spots in your defences. We always lead with human expertise, making sure you’re getting real-world value from our AI tools. Our comprehensive testing approach ensures your AI security investments deliver genuine protection against real-world threats. 

Discover how our penetration testing services can strengthen your AI-enhanced security posture.