Publication Date
Enhance Your Cyber Resilience with artificial intelligence
Experts in artificial intelligence at ITI present five use cases in which AI can support a company’s cyber resilience efforts. With advanced technologies and sophisticated machine learning models, these applications demonstrate how artificial intelligence can transform cybersecurity and protect businesses against growing threats.
Detection of Anomalies and Intrusions
Imagine a vast corporate network where thousands of employees access systems daily. Generally, each user follows a certain access pattern (such as work hours and applications used).
An artificial intelligence model based on unsupervised learning (such as Isolation Forest or Autoencoders) can identify abnormal activity, like an employee logging in at 3 AM from a foreign country. This could indicate that an account has been compromised by an attacker.
Impact on IT teams:
Rather than waiting for an analyst to detect this suspicious activity, AI can automatically alert the cybersecurity team or even temporarily block access to prevent any potential damage.
Classification of Threats – Phishing and Malware
An employee receives an email that looks like internal communication but contains a fraudulent link aimed at stealing their credentials. A machine learning model, trained on millions of emails (such as Random Forest, XGBoost, CNN), can analyze the text, sender, and attachments to determine whether the email is legitimate or a phishing attempt.
Impact on IT teams:
Rather than each employee having to guess whether an email is dangerous, AI can automatically flag suspicious messages, thereby reducing risks and protecting the company from Business Email Compromise (BEC) attacks.
User and Entity Behavior Analytics (UEBA)
An employee in the accounting department accesses technical databases and downloads large files. It is possible that this is related to data theft preparation or that the employee’s account has been compromised by a cybercriminal. Deep learning models (RNN, LSTM) can analyze user behavior over several weeks to detect anomalies.
Impact on IT teams:
The AI can alert the cybersecurity team and request additional authentication (for example, double validation by phone) before allowing access to sensitive files.
Detection and Automated Response to Cyberattacks (SOAR + AI)
A company receives thousands of security alerts every day. Most are harmless, but a few are serious threats. A reinforcement learning model can learn to prioritize alerts and automate initial responses:
- If an attack is suspected, the AI can immediately block the source IP.
- If a virus is detected, the AI can isolate the infected workstation from the network.
Impact on IT teams:
This reduces the workload on cybersecurity analysts, allowing them to focus on the most critical threats instead of manually managing each alert.
Threat Intelligence and Attack Predictions
A model of artificial intelligence analyzing dark web forums and cyber threat databases could identify discussions concerning an imminent attack against specific sectors such as hospitals or banks. Graph Neural Networks (GNN) allow examining the relationships between different threats and predicting future attacks.
Impact on IT teams:
Instead of simply reacting to attacks, companies can adjust their defenses based on detected trends, thereby enhancing their cyber resilience proactively.
