How is AI Used in Cybersecurity?

The cybersecurity landscape is constantly evolving, driven by the increasing sophistication of cyber threats. As organizations become more reliant on digital infrastructure, the need for robust and adaptive security measures has never been more critical. Enter artificial intelligence (AI) and machine learning (ML) — technologies revolutionizing how we approach cybersecurity.

10 ways AI and ML are used in cybersecurity

AI is used in cybersecurity to enhance the detection, prevention and response to cyber threats. Here are key areas where AI is applied:

1. Threat detection

Anomaly detection: AI algorithms establish baselines for normal behaviour and detect deviations that might indicate potential threats. Including monitoring network traffic, user behaviour and system activities.
Malware detection: AI models analyze the characteristics and behaviour of files to identify known and unknown malware, including new and evolving threats. Which includes signature-based and behaviour-based detection.
Intrusion detection systems (IDS):AI enhances IDS by identifying unusual patterns in network traffic that may indicate an intrusion.

2. Threat analysis

Automated analysis:AI can process large volumes of security data to identify patterns and correlations, speeding up threat analysis and reducing the time to detection.
Behavioural analysis: AI assesses user and entity behaviour to detect anomalies that could indicate compromised accounts or insider threats.
Root cause analysis: AI helps trace the origin and path of a cyberattack, providing insights into how the attack occurred and identifying vulnerabilities exploited by the attackers.

3. Incident response

Automated response: AI can trigger automated responses to certain types of threats, such as isolating affected systems, blocking malicious IP addresses and applying patches.
Remediation recommendations: AI provides actionable insights and recommendations for mitigating threats based on the analysis of security incidents.
Incident triage: AI assists in the initial triage of security alerts, categorizing and prioritizing incidents to ensure the most critical threats are promptly addressed.

4. User and entity behaviour analytics (UEBA)

Behavioural baselines: AI establishes what constitutes normal behaviour for users and systems. Deviations from these baselines can trigger alerts for potential security breaches.
Insider threat detection: AI can detect potential insider threats, whether malicious or unintentional, by analyzing user behaviour.

5. Vulnerability management

Predictive analytics: AI predicts which vulnerabilities are at risk for potential exploitation based on historical data and emerging threat trends, helping organizations prioritize patching efforts.
Vulnerability scanning: AI enhances the detection and assessment of vulnerabilities in systems and applications, often integrating with existing vulnerability management tools.

6. Phishing detection

Email filtering: AI algorithms analyze email content, context and metadata to detect phishing attempts more accurately than traditional methods.
URL analysis: AI assesses URLs in real time to determine if they lead to malicious websites, protecting users from phishing and other web-based threats.

7. Fraud detection

Transaction monitoring: In financial systems, AI monitors transactions for signs of fraud by identifying suspicious activities, such as unusual spending patterns or account access from unusual locations.
Identity verification: AI improves identity verification processes by analyzing biometric data and other indicators.

8. Threat intelligence integration

Data ingestion and analysis: AI ingests and analyses vast amounts of threat intelligence data from various sources, identifying emerging threats and trends.
Automated updates: AI systems automatically update security measures based on the latest threat intelligence, ensuring defences remain current.

9. Security Operations Centre (SOC) efficiency

Alert prioritization: AI helps reduce alert fatigue by prioritizing alerts based on severity and context, allowing security analysts to focus on the most critical threats.
Incident triage: AI categorizes and assesses security incidents, streamlining the response process and improving SOC efficiency.

10. Reducing stress on IT teams

Speed and efficiency: AI processes data and detects threats faster than human analysts, enabling real-time or near-real-time responses.
Accuracy and precision: AI reduces false positives and negatives, providing more accurate threat detection and analysis.
Scalability: AI can handle large volumes of data and scale across complex networks, making it suitable for organizations of all sizes.
Resource optimization: By automating routine tasks, AI allows human security experts to focus on more complex and strategic aspects of cybersecurity.

Overall, AI significantly enhances cybersecurity by providing robust, scalable, and efficient defence mechanisms against the ever-evolving landscape of cyber threats.

Source: ID Agent