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Context & Scope
Security monitoring is a critical function that involves continuously observing an organisation's digital assets, networks, and systems for potential threats or vulnerabilities. Traditionally, human security analysts perform this role by manually reviewing logs, analysing alerts, and responding to incidents as they occur.
- Financial Services: Monitoring transaction patterns to detect potential fraud or money laundering activities.
- Healthcare: Analysing access logs to identify unauthorised attempts to view patient records.
- Manufacturing: Monitoring industrial control systems for signs of cyber attacks or operational anomalies.
- E-commerce: Detecting and mitigating DDoS attacks on web servers during high-traffic periods.
- Energy: Monitoring smart grid systems for potential cyber intrusions or infrastructure vulnerabilities.
AI Solution Overview
- AI continuously ingests data from various sources (network logs, security devices, applications)
- Machine learning algorithms analyse data in real-time, looking for anomalies or known threat patterns
- When a potential threat is detected, AI assesses its severity and potential impact
- Based on predefined rules and learning, AI initiates appropriate response actions
- AI alerts security teams with detailed threat information and recommended actions
- Security personnel review AI-generated alerts and confirm or modify response actions
- AI learns from human decisions to improve future threat detection and response
If needed at any point: • AI can escalate complex or high-risk threats to human analysts • Human analysts can override AI decisions and manually control responses • AI can adapt its monitoring parameters based on feedback from security teams
Human vs AI
| Human Intelligence (HI) | Artificial Intelligence (AI) |
|---|---|
| HI can only monitor a limited number of events simultaneously | AI can monitor millions of events in real-time across multiple systems |
| HI may miss subtle patterns or correlations in vast amounts of data | AI can identify complex patterns and correlations across diverse data sources |
| HI can become fatigued during long monitoring sessions, potentially missing threats | AI maintains consistent vigilance 24/7 without fatigue |
| HI response times can vary based on analyst availability and workload | AI can respond to threats instantly, initiating automated defensive measures |
| HI may struggle to keep up with rapidly evolving threat landscapes | AI can continuously update its threat detection models based on global threat intelligence |
| HI can be subjective in threat assessment, leading to inconsistent responses | AI applies consistent, data-driven criteria for threat assessment and response |
| HI requires extensive training to recognise new types of threats | AI can quickly adapt to new threat types through machine learning |
| HI can handle a limited number of simultaneous incidents | AI can manage and prioritise multiple security incidents simultaneously |
| HI may overlook threats during high-volume periods | AI's performance remains consistent regardless of data volume |
| HI relies on memory and experience for threat context | AI can instantly access and analyse vast historical data for context |
Addressing Common Concerns
False positives overwhelming security teams: AI systems are trained to minimise false positives and can be fine-tuned over time. They also prioritise alerts, ensuring high-risk threats receive immediate attention.
AI missing novel or sophisticated attacks: While it's true that AI may struggle with completely new attack types, it excels at detecting subtle anomalies that could indicate novel threats. Human analysts remain crucial for interpreting these anomalies.
Over-reliance on AI leading to complacency: AI is designed to augment human expertise, not replace it. The most effective security strategies combine AI's speed and pattern recognition with human intuition and decision-making.
AI making critical security decisions autonomously: While AI can initiate immediate responses to clear threats, it's configured to involve human analysts in complex or high-stakes situations. Humans always retain final decision-making authority.
Data privacy concerns with AI analysis: AI systems can be designed to analyse metadata and patterns without accessing sensitive content, adhering to data protection regulations. Proper data governance ensures AI operates within defined privacy boundaries.
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