Cybersecurity Analytics in Saudi Arabia evolved from Security Incident and Event Management (SIEM) to meet the need for greater security across the business; more context and more insights. There are 3 key components: Security Incident and Event Management (SIEM), Behaviour Anomaly Detection (BAD or UEBA) and Threat Intelligence.
Security analytics isn’t one particular type of tool or system. It is a way of thinking about cybersecurity proactively. It involves analyzing your network’s data from a multitude of sources in order to produce and maintain security measures. It’s all about aggregating data from every possible source and finding the “forests” that all of those “trees” of logs and other recorded details are a part of.
Cybersecurity Analytics in Saudi Arabia Sources and Tools
Here are some of the different types of data sources which can be used in your cybersecurity analytics practices:
- Cloud resources
- User data acquired from endpoints
- Logs from network security appliances, such as firewalls, IPS, and IDS
- Network traffic and its patterns
- Identity and access management logs
- Threat intelligence
- Geolocation data
- Mobile devices and storage mediums connected via WiFi, Ethernet, and USB
- Antivirus applications
- Business-specific applications
There are some types of tools which your network can deploy which pertain to cybersecurity analytics. They include:
- Code analysis applications to find vulnerabilities in software and scripting
- File analysis tools to explore files in ways which may go beyond malware detection
- Log analysis applications for firewalls, IDS, IPS, networked print devices, servers, and endpoints
- SOC (security operations center) specific applications to organize data in a way which is useful for their functions
- DLP (data loss prevention) tools
Security Analytics AI and Machine Learning
- When AI is usefully deployed for cybersecurity analytics, it can be used to scan your entire IT environment to find patterns and identify anomalies.
- Well implemented AI can take a lot of the calculation and identification work off of the shoulders of your human security analysts so that they can direct their efforts to areas where human thinking is more effective.
- People’s brains can tire of repetitive and tedious work, whereas AI can deal with loads of tedious data without mental fatigue.
- All of the supposedly boring details won’t be missed by properly configured advanced computer systems!
- Machine learning can be implemented by your AI and monitoring systems to learn from data and results which are accumulated over time.
- Machine learning can have both supervised and unsupervised applications according to your specific needs.
- Supervised machine learning can analyze structured data for clear algorithms and rules.
- Unsupervised machine learning can analyze unstructured data from sources such as SIEM and general scans.