Dear Wisam,

The answer is complex and would require specific use cases to find a more accurate solution.

As the Cybersecurity Manager in a technology company, I can suggest various practical applications that integrate AI to strengthen your security perimeter or make strategic decisions more efficiently.

Let me present a use case:

We have an organization with 100 endpoints, which may include mobile devices. From a threat intelligence perspective, you want to determine if there are any risk patterns affecting an endpoint that could pose a greater threat for lateral movements.

One valid option for this case is to leverage the power of a SIEM-XDR to gain visibility of the entire perimeter by integrating various sources such as user logs, AV, FW, etc. this can help achieve traceability of potential attacks and enrich the information with pre-trained AI models. Large manufacturers like AlienVault and QRadar have been implementing this for some time, but if you want a practical and free way to monitor security using AI to threat monitoring, consider integrating Wazuh with Chat GPT, here are several libraries available for this purpose; personally, I have integrated it and found it helpful in making low-level decisions.

Here is an example of integration: https://github.com/AnonymousWP/Wazuh-ChatGPT-integration/blob/master/custom-chatgpt.py

I hope I have given you a practical insight into the use of AI to enhance cybersecurity in a corporate network.

Regards,

Here are some ways AI can be applied in cybersecurity for a corporate network:

Threat Detection and Anomaly Detection:

AI can analyze network traffic patterns and user behavior to detect anomalies that may indicate a security threat.

Machine learning models can be trained to identify abnormal patterns that deviate from regular network activities, helping in the early detection of potential threats.

Behavioral Analysis:

AI algorithms can establish a baseline of normal behavior for users and devices. Deviations from this baseline could signal potential security incidents.

Behavioral analytics can help identify insider threats, compromised accounts, or unauthorized access.

Endpoint Security:

AI-powered endpoint protection systems can analyze files and activities on endpoints to detect and prevent malware or malicious activities.

Behavioral analysis on endpoints can identify unusual patterns, providing an additional layer of defense against advanced threats.

Security Automation and Orchestration:

AI can automate routine security tasks, such as patch management, threat response, and incident investigation, reducing the response time to security incidents.

Security orchestration platforms can integrate different security tools and streamline incident response workflows.

Phishing Detection:

AI algorithms can analyze emails, URLs, and attachments to identify phishing attempts.

Natural Language Processing (NLP) can be employed to analyze email content and detect social engineering techniques.

Network Traffic Analysis:

AI can monitor and analyze network traffic in real-time to identify patterns indicative of cyber threats.

Deep learning models can identify patterns associated with known and unknown threats.

Vulnerability Management:

AI can assist in identifying vulnerabilities in the network and prioritize them based on potential impact and risk.

Automated scanning and analysis can help organizations stay proactive in addressing security weaknesses.

User Authentication and Access Control:

AI-driven authentication systems can continuously assess user behavior to detect anomalies and potentially compromised accounts.

Access control systems can use AI to dynamically adjust permissions based on user behavior and risk profiles.

Incident Response:

AI can enhance incident response by automating the identification, containment, eradication, and recovery phases.

Machine learning models can assist in contextual analysis during incident investigations.

Adaptive Security Frameworks:

AI enables security systems to adapt and learn from evolving threats, providing a dynamic defense mechanism.

Adaptive security frameworks use AI to continuously assess and adjust security measures based on the changing threat landscape.

As a language learning model it can easily be taught to automatically detect and delete even more sophisticated fraudulent emails that human users may accidentally act on due to the fact that such emails often target their emotions that LLM's lack.

Dear Doctor

"In cases of cyber threats, AI can assist in automating response procedures. This may include system shutdowns, blocking network access, or even launching countermeasures against attackers. Automating these processes can help manufacturers to respond more swiftly and lessen the impact of any attack."