Introduction
Cybersecurity focuses on safeguarding your online assets, from online banking information to personal photos on your laptop. However, this industry is undergoing a major change by integrating AI into traditional cybersecurity methods. AI involves simulating human intelligence in machines, enabling them to think, learn and solve problems.
95% of cybersecurity professionals agree that AI-powered solutions will level up their organisations’ defence (Darktrace Survery, 2024). Today’s cybersecurity landscape is broader than ever. AI enhances security by automating threat detection and response, while machine learning learns from past incidents to improve future responses. However, there is a double-edged effect AI can transform not just the threat of a cyber attack but the protection against one.
5 uses of AI in Cybersecurity
Threat Detection and Prevention
AI can continuously analyse network traffic and user behaviour to detect unusual patterns, identifying potential threats such as malware or phishing attempts in real time. Unlike traditional tools that rely on predefined rules, AI-based systems can dynamically adjust to new threats as they emerge, reducing response times and minimising the impact of attacks.
Example: AI-driven platforms like Darktrace use machine learning algorithms to establish a “pattern of life” for network users and detect deviations that may indicate an active threat.
Automated Incident Response
AI’s ability to analyse historical data enables predictive threat modelling. By using patterns from past incidents, AI systems can forecast potential security breaches or vulnerabilities, allowing organisations to take proactive measures to prevent attacks before they occur.
AI can automate responses to security incidents by isolating compromised systems, blocking malicious IP addresses, and initiating system recovery actions. This automation reduces the need for human intervention during critical moments, allowing for faster resolution and minimising downtime.
Example: Security Orchestration, Automation and Response (SOAR) platforms like Splunk Phantom or Cortex XSOAR integrate AI to streamline response workflows, ensuring that critical incidents are handled promptly and efficiently.
Predictive Analysis for Threat Forecasting
AI’s ability to analyse historical data enables predictive threat modelling. By using patterns from past incidents, AI systems can forecast potential security breaches or vulnerabilities, allowing organisations to take proactive measures to prevent attacks before they occur.
Example: AI tools such as CrowdStrike Falcon utilise machine learning to detect emerging threats by analysing billions of events and identifying high-risk behaviours across networks.
Fraud Detection
AI algorithms can identify fraudulent activities by analysing large volumes of transaction data and recognising anomalies, such as unusual purchasing behaviours or suspicious login attempts. This real-time detection is particularly effective in preventing financial fraud and identity theft.
Example: Visa and Mastercard deploy AI-driven fraud detection systems that assess millions of transactions per minute, flagging potentially fraudulent behaviour for further investigation.
Network Security Monitoring
AI can continuously monitor network infrastructure 24/7, identifying and neutralising vulnerabilities before they can be exploited by malicious actors. AI’s ability to operate without fatigue makes it a valuable asset in preventing system downtimes and securing critical network elements.
Example: Tools like Palo Alto Networks’ Cortex use AI to analyse vast amounts of network data in real-time, identifying unusual patterns and mitigating risks before they compromise the system.
With AI having a massive impact on the cybersecurity industry for both attackers and individuals protecting themselves. It is important to understand the impact and effect of AI to be protected in the future.
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