Behavioral analytics, or BA, is becoming increasingly useful in the world of cybersecurity. With cyber threats ever-evolving and with businesses of all shapes and sizes striving to keep up, BA is helping companies future-proof their data, networks, and other assets with greater confidence.
BA revolves around the use of artificial intelligence, or AI, and machine learning, or ML, to train on threat trends. It trains AI on data sets to learn about and establish the status quo, therefore also learning to spot unusual network behavior and data processing patterns.
If it makes an incorrect decision or raises a false flag, it’s trained to avoid such mistakes in the future.
This allows BA to support in-depth auditing reports, help influence cybersecurity decision-making, and even provide real-time threat analysis and recommendations.
Understanding Key Cybersecurity Tools
Cybersecurity through BA breaks down into four main tools or use categories:
- EDR (Endpoint Detection and Response). Behavioral EDR analyzes user activities at endpoints to spot potential threats from inside a network. For example, BA EDR can be trained to raise flags if certain users download excessive amounts of data.
- SIEM (Security Information and Event Management). SIEM refers to collecting data through end-user hardware to devise plans of action. For example, companies use SIEM to collate data and produce real-time visualizations where it’s easy to see harmful network behavior before it causes serious damage.
- UEBA (User and Entity Behavior Analytics). UEBA works hand in hand with SIEM to build user and hardware profiles, creating “baselines” or status quo templates. UEBA tools use SIEM to absorb user behavior data to monitor and report if any activities deviate from the baselines set.
- NTA (Network Traffic Analysis). NTA specifically observes traffic patterns and reports potential anomalies. For example, by analyzing flow data, NTA can report unexpected communications or access requests, or raise flags if traffic volumes spike without due cause or reason.
Comparative Analysis of Tools
Let’s examine each of the tools further and consider their strengths and weaknesses in detecting and preventing threats.
- EDR: EDR solutions are reliable for reporting potential threats with efficiency, particularly as companies can use them to measure critical endpoints. With that said, however, EDR tools largely only measure endpoints, meaning you cannot rely on EDR alone to watch for potential threats outside the network.
- SIEM: Using BA and SIEM means you have a powerful threat detection and response protocol solution that’s safe to leave working fairly autonomously. SIEM can cut down unnecessary manual handling, and is also highly effective at streamlining complex activity logs. However, SIEM tools are time-consuming and complex to set up and maintain.
- UEBA: UEBA can pick up where EDR leaves off, effectively detecting external threats to prevent DDoS and brute-force attacks. What’s more, using BA with UEBA means there’s less need to hire specialists to maintain your security perimeter (although it’s always prudent to hire experts for vulnerability scanning and managed protection). The major downside to UEBA is it needs significant data training before becoming effective.
- NTA: NTA is an ideal solution as an automated, always-on threat response, and it offers clear visibility of activity across a potentially broad and complex network. It can also help to train and inform people about traffic threats at a granular level. Downsides, however, include the fact you largely need to train NTA and BA on extensive historical data, and it can make for a costly hardware investment. Considering the ongoing cost of device management alone is already affecting your overheads, it pays to find a good value security solution.
Practical Integration Tips for Cybersecurity Professionals
Implementing efficient and reliable BA cybersecurity is not an overnight process. It’ll take partnering with cybersecurity experts, for one, to safely ensure your network and digital assets are secure under a machine learning environment.
In the meantime, however, here are a few quick best practices to consider when setting up and managing BA cybersecurity:
- Don’t shy away from big data. The more information BA tools have to work with, the more accurate and efficient they will be at building a status quo – and learning what to fight back against.
- Don’t set and leave BA tools. Work with a cybersecurity expert to manage the hardware and monitor your security strategy over time. Be ready to change tools and approaches if, in time, your setup doesn’t prove beneficial.
- Don’t be afraid to integrate. As the case study from Securonix above shows, it’s possible, if not advisable, to blend the old with the new. With a reliable cybersecurity team, you may not need to remove legacy systems to make way for a completely new installation.
- Compare and contrast solutions. Look carefully into the benefits of BA tools you’re considering, and apply them to specific use cases. Look before you leap, and read industry advice and reviews before committing.
- Test your defenses. Even the most advanced analytics can miss certain weaknesses. Regular penetration testing complements your BA tools by simulating real-world attacks, uncovering vulnerabilities, and helping you strengthen areas that automated monitoring might overlook.
- Educate your people. Remember that cybersecurity is not just about technology, it’s also about people. Investing in employee awareness and training is critical. Many organizations are now using automated video content to educate teams on emerging threats and protocols, providing timely, consistent information without adding manual workload.
Conclusion
Behavioral analytics and machine learning are making cybersecurity more efficient and easier to manage across the board.
However, it pays to look carefully into different BA tools and to consider partnering with a cybersecurity firm. Just as it makes sense to compare mobile device management solutions and productivity tools, it pays to invest in the best analytical security strategies to suit your firm.
The future of cybersecurity is certainly rooted in AI – meaning now is the time to start embracing ML and advanced analytics to protect your business better.
