Published Aug 07, 2023 by Xiph
Traditional solutions may only sometimes suffice in the ever-evolving landscape of cyber security. Organisations can use the synergy between artificial intelligence (AI) and human expertise to protect their digital assets from cyber threats and malicious actors. Here are ten ways to use AI to bolster your cyber security.
1. Behavioural biometric authentication
Use AI-driven behavioural biometrics to analyse user interactions on your systems and applications to detect threats in real-time. Behavioural biometrics continuously monitors the micro-patterns of users' behaviours and learns to recognise individuals based on the unique way they move across networks. It creates a unique identification for each user based on their digital physical and cognitive behaviour. For example, a person has specific behavioural patterns around login times, physical interactions, device behaviours, geolocations, transactions, biometrics and beyond. Suspicious patterns or activity get flagged almost instantly and investigated without disrupting legitimate users.
2. Anomaly detection with AI
Deploy AI algorithms to identify unusual patterns or outliers in data to detect anomalies. Using historical data or statistical models, these systems establish a ‘normal’ behaviour baseline. Each new data point introduced is then compared to the established baseline, and a deviation metric is calculated. The data point is flagged as an anomaly if the deviation exceeds a predefined threshold. Anomaly detection is widely used in various fields, including cyber security, fraud detection, network monitoring, and industrial quality control, to quickly identify unusual events or behaviours that may indicate potential threats or issues.
3. AI-enhanced endpoint protection
You can integrate AI into your endpoint security program to analyse and correlate endpoint data. Since most organisations use multiple endpoint systems, machine learning can scan the entire perimeter for vulnerabilities, consolidate visibility, automate encryption, and deploy targeted patches and protections. AI endpoint security can also apply to compliance reporting and risk and fraud prevention in industries with strict privacy regulations like finance, health, and education.
4. Predictive threat intelligence
AI algorithms can predict and prioritise potential cyber threats to your organisation. It collects data from diverse sources, including threat feeds, logs, and dark web forums. This data is then analysed to identify patterns, vulnerabilities, and malicious behaviour.
Security teams can create threat models and conduct behavioural analysis to understand potential attack scenarios. This approach also includes historical data analysis to track the evolution of threats. Predictive threat intelligence assigns threat scores to prioritise responses and offers early warnings for emerging threats. With continuous monitoring and adaptation, businesses can effectively strengthen their cyber defences and proactively protect their assets and data from cyber attacks.
5. Automated security patching
Automated security patching uses AI solutions and software tools to automatically detect and deploy updates to fix known vulnerabilities in an organisation's systems and applications. It continuously scans for vulnerabilities, retrieves relevant patches, and deploys them without human prompts. The system then verifies the patch installation and provides detailed reports for compliance. Businesses can automate this process to better protect their systems from potential threats, reduce the risk of security breaches, and save time and resources on manual patch management tasks.
6. AI-powered phishing detection
AI-powered phishing detection uses advanced machine learning algorithms to analyse email content, URLs, and sender behaviour to flag the characteristics of phishing attempts. These smart systems learn from vast datasets of legitimate and malicious emails to recognise patterns, suspicious URLs, and social engineering techniques. They even evaluate the sender's reputation, language, and context to assess an email's legitimacy. AI algorithms can also analyse embedded links and attachments for malicious content. When a potential phishing email is detected, the system can flag it for review or automatically quarantine it.
7. AI-driven network security
AI-driven network security utilises artificial intelligence and machine learning to enhance the protection of computer networks. It involves analysing vast network data, including traffic patterns, user behaviour, and potential threats. AI algorithms learn from this data to detect anomalies, identify malicious activities, and predict potential cyber attacks. AI-driven security systems can autonomously respond to threats by blocking suspicious traffic, isolating compromised devices, or initiating response measures like data backups and recovery.
8. Automated remediation
AI-driven automation can respond to certain low-level security incidents automatically. For instance, if an AI detects a suspicious login attempt from an unrecognised location, it can temporarily block the source IP address, reducing the attack surface while waiting for further investigation. Another example of AI-driven automation in incident response is in the case of distributed denial-of-service (DDoS) attacks. When an AI system detects a sudden surge in network traffic from various IP addresses indicative of a potential DDoS attack, it can automatically trigger traffic filtering or diversion mechanisms to mitigate the impact.
9. Malware analysis and mitigation with AI
Malware analysis and mitigation work to recognise and neutralise malicious software (malware). It involves collecting a malware sample and analysing its code and behaviour in a controlled environment like a sandbox or virtual machine. AI can then examine the malware's functionality, communication channels, and potential impacts.
Security teams can then use this analysis to devise a mitigation or response plan, such as updating firewalls or developing patches for vulnerabilities. The aim is to prevent further infections and develop countermeasures tailored to each specific malware or cyber intrusion.
10. Data loss prevention (DLP)
Data loss prevention monitors, detects, and prevents sensitive data from being leaked, lost, or stolen. DLP tools identify and classify sensitive data throughout an organisation, including personal information, financial data, intellectual property, etc. They continuously monitor data in transit, at rest, and in use. It tracks data access, movement, and transfers, flagging any suspicious activities. DLP applies predefined policies or rules to prevent unauthorised data access, block data exfiltration attempts, or encrypt sensitive data. When policy violations occur, DLP triggers alerts and may take automated actions to stop data leakage and protect critical information.
A final word
Businesses can stay one step ahead and maintain a robust security posture in an ever-changing digital landscape by leveraging the capabilities of AI in their cyber toolkit. Machine learning can provide unmatched protection against modern threats, from real-time threat detection to advanced malware analysis. For more information on how to use AI to bolster your cyber security, contact us at [email protected].
Posted in: Security