What Is Cybersecurity Analytics?

An organisation needs cybersecurity analytics to determine the cause of an incident and collect data for future investigations. Analytics can be used for proactive cybersecurity to stop an ongoing threat or for reviewing past incidents to determine the best steps going forward to ensure a specific incident doesn’t happen again.

Behavioural analytics can be used to determine outcomes or detect potential threats. Recent cybersecurity strategies urge organisations to move towards a “shift left” approach towards data protection. Cybersecurity analytics uses machine learning (ML) and artificial intelligence (AI) to detect threats before they damage the organisation. These systems change data protection strategies to a proactive “shift left” approach to monitoring the environment instead of reacting after a cyber-incident.

A few reasons why cybersecurity analytics are needed:

Proactive rather than reactive cybersecurity. Several security systems will alert administrators to a data breach, but analytics monitor the environment for anomalies to alert administrators of suspicious activity before it becomes a data breach.

Complete views of network traffic. Cybersecurity analytics detect activity as it’s happening to give administrators a better view of network traffic. If a new device is added to the network or user behaviour patterns do not match current benchmarks, an administrator will have enough information to investigate.

Collection of data to show a return on investment for cybersecurity efforts. Every operational budget needs a return on investment to show that cybersecurity infrastructure is saving money on threat detection.

Most organisations are unaware of the risks introduced to their corporate network, including insider threats from employees and trusted users. Good cybersecurity analytics tools help with discovery so corporations can take necessary precautions to remediate vulnerabilities. Cybersecurity analytics tools are also used in risk analysis, intrusion detection and incident response, threat intelligence and automation. Legal and law enforcement agencies use them to investigate threats and vulnerabilities in the aftermath of a data breach, and they can be used to show compliance with regulatory requirements.

Every cybersecurity analytics platform should integrate well with an organisation’s system to realise its benefits. Several monitoring tools are available, and many organisations believe that a security information and event management (SIEM) application is adequate for monitoring the network. But, most SIEM tools are reactive and not proactive monitoring. A SIEM is still beneficial but is optimally beneficial in combination with analytics tools.

A few benefits of using cybersecurity analytics include:

Prioritisation of alerts. Not all threats are created the same. Some are critical and must be remediated quickly, and others are low-priority compared to a critical threat. Good cybersecurity analytics makes it easy for administrators to prioritise their efforts to minimise damage.

ML-based threat intelligence. Automated threat intelligence is a form of cybersecurity used to scour the web for zero-day threats and understand the latest attacks in the wild. The machine-learning component enables cybersecurity analytics to keep administrators informed even when a specific threat hasn’t been seen in the wild yet.

Proactive detection. Any cybersecurity strategy working with reactive measures leaves the environment open to damage. Reactive cybersecurity attempts to mitigate the damage done, while proactive detection stops a threat before it can damage the environment.

Incident investigations and data collection. Whether a threat was immediately detected and stopped or a successful attack must be remediated, an organisation needs data collection and investigation features to determine the extent of damage. Investigation data can be sent to law enforcement, and it helps administrators improve cybersecurity infrastructure to avoid the same mistakes.

The key features of a security analytics platform are discovery and data collection using machine learning. Every environment is different, and a good security analytics platform uses ML to mould its discovery and monitoring to the specific organisation. Its data collection is helpful for legal teams in investigations, making it useful for a proactive and reactive cyber-incident response.

Discovery of an organisation’s attack surface is another feature used to determine risks and remediate current vulnerabilities. Security analytics platforms then give administrators the ability to continually monitor the environment and get alerts on potential threats and vulnerabilities so that they can quickly remediate them. Some threats can be automatically stopped without any interaction from an administrator. The platform still sends alerts and provides information about the threat for future investigations, but automatically remediating issues can stop threats before they can do any damage.

Another key feature of a good cybersecurity analytics platform is the ability to work with large data collection stores where machine learning digests information to provide monitoring and alerting capabilities. Instead of working with just internal data, security analytics platforms monitor the web on various clearnet and darknet sites to discover current threats and trends.

Tools used in cybersecurity analytics bring several benefits to an organisation that cannot be found in other traditional tools. Most benefits are universal across all organisations and industries, but administrators should seek tools with the features necessary to support cybersecurity strategies, disaster recovery, risk discovery and investigation efforts.

A few benefits are:

Monitoring of network traffic. Analysis of network traffic across internal and cloud resources helps identify threats as they happen instead of after damage is done.

Endpoint threat protection. Every user device is a risk to the environment, so cybersecurity analytics tools discover and monitor laptops, smartphones, desktops, IoT and other mobile devices connected to the network.

Insider threat detection. Employees can be cyber-threats either from malicious intent or unintentional mistakes. Cybersecurity analytics tools monitor user behaviours to detect insider threats.

Detection of data exfiltration. After a compromise, attackers will exfiltrate data by exporting it to another location, usually externally. Cybersecurity analytics monitors the network for data exfiltration as it happens and alerts administrators.

Compliance. Every organisation has some compliance regulatory standards that it must follow, and a security analytics tool will help automatically follow many of the best practices highlighted in these compliance guidelines.

“Cybersecurity” is an umbrella term that covers any data protection from threats, while “data analytics” is a specific strategy used to make informed data-driven decisions on threat detection and remediation. Data analytics uses large amounts of information collected from various locations to feed ML algorithms. ML algorithms use data to provide insight into the health and security of an organisation’s environment.

Data analytics can be a component in cybersecurity protection, but it’s not everything in a cybersecurity strategy. It’s one component in the various tools used to actively monitor networks, perform security research and remediate threats as they are found in the environment.

Although data analytics is just a component of cybersecurity, it’s also important for organisations to find tools and strategies that can work with large data silos. To help with data collection, small organisations work with cloud-based analytics that have their own data collection standards. For large organisations, cybersecurity staff will help build a solution around the current environment and find strategies that conform with compliance regulations.

Just like data analytics, big data is an element of cybersecurity. Cybersecurity encompasses all forms of data protection and digital threat remediation. Big data is also a component of analytics. It’s a term given to large data silos used in machine learning, artificial intelligence and analytics platforms to provide data-driven decision-making. Big data can be used in more than analytics, so it should only be used as a part of a strategy and not your entire cybersecurity strategy.

Without big data, cybersecurity analytics platforms could not provide decision-making tools for organisations and administrators. Big data can be collected and stored in-house, or organisations can use platforms with their own storage to display information in a cloud environment. The more data available, the better chance of more accurate results. Machine learning and artificial intelligence rely heavily on large datasets to model information accurately.

Big data should not be confused with analytics. Analytics and machine learning used in analytics rely on big data, but it’s a component and not the complete picture. Data should be verified and collected from reliable sources, or analytics could have inaccurate or questionable information. The wrong data models could inaccurately train machine learning algorithms used in cybersecurity analytics.

Although machine learning and artificial intelligence have been a component of cybersecurity analytics for years, platforms that use analytics tools are still in their infancy. The future of cybersecurity analytics requires more investigation into how threats are deployed and managed so that cybersecurity tools can be updated to deal with them.

Data and user behavioural patterns are critical factors in cybersecurity analytics, but attackers continue to change their own strategies to blend in with other users so that analytics tools cannot detect a data breach. As more users work from home, detecting threats is more difficult for administrators tasked with ensuring the continuity and security of the corporate environment.