As cyber attackers continue to evolve their tactics and technologies, businesses need to respond by adopting advanced approaches that cater to current realities.
Traditional signature-based approaches to data security have been ineffective so far. However, AI-driven behavioral analytics is a very promising area for data detection and response.
This article explores how behavioral analytics works in the enterprise data security environment as well as how you can access this power to enhance your system protection.
How Behavioral Analytics Works
Here’s a brief explanation of the procedures applied in behavioral analytics for detecting and responding to breaches.
- The process starts with collecting relevant data from various sources. These include logs, network traffic, user activities, endpoint data, and other event data.
- Next, a baseline of normal behavior is established by analyzing historical data and pulling patterns of secure use. Factors that may be considered include user activity, network behavior, access patterns, etc. Of course, these are not analyzed as isolated events. Rather, they are analyzed in the broader context in order to differentiate between genuine threats and simply unusual (but secure) activities.
- Once the baseline is established, the system gets to work and starts monitoring data continuously to find anomalies. Machine learning algorithms using statistical models, clustering techniques, neural networks, and other advanced methodologies enable the system to adjust the baseline to adapt to evolving behavior and emerging threats.
- The system assigns real scores to detected anomalies based on their security, potential impact, and the context in which they occur. This determines whether to alert the IT team as well as the alert level to apply. The security team then investigates the alert and prioritizes their response efforts.
- Behavioral analytics systems, since they are AI-based, continuously learn and adapt to new data. This is an automatic and iterative process that helps the algorithms to improve their accuracy over time.
Benefits of Behavioral Analytics
The primary strength of behavioral analytics lies in its ability to analyze vast amounts of data from diverse studies. Aggregating and correlating this much information provides a wide range of benefits for enterprise data security.
Early Detection of Threats
Being able to capture even the slightest deviations from established baselines of normal behavior, behavioral analytics systems enable early detection of threats.
This also applies to zero-day attacks, which have proven resistant to advanced firewalls and other traditional security solutions. Enterprise business moves fast, and being able to capture threats in real-time significantly helps reduce the cost and impact of a breach.
Improved Accuracy in Threat Detection
Behavioral analytics platforms are better placed to capture threats than traditional signature-based methods because they are equipped to continuously and automatically adapt and improve their detection capabilities.
The integration of AI and machine learning into data detection and response, thus, results in higher accuracy rates.
Combating Advanced Persistent Threats (APTs)
APTs are a particularly challenging kind of threat to address, but this hardly applies when using an equally sophisticated behavioral analytics system.
These systems can identify subtle patterns and anomalies such as unusual file access, privilege escalation, data exfiltration, etc. that may be indicative of an active attack on the organization’s network.
Reduced False Positives
Traditional security solutions are notorious for bringing up false positive results. This leads to alert fatigue and sends members of the IT team on a wild goose chase, diverting key resources that should have been dedicated to mitigating serious threats.
Behavioral analytics, on the other hand, can be used to build a comprehensive understanding of normal behavior so as to accurately tell genuinely suspicious activities. Since they are AI-based, there may still be some false positives, but at least, they’ll be fewer for the cybersecurity team to handle.
Improved Incident Response Time
The result of the improved detection capabilities of behavioral analytics is that an IT team is better equipped to coordinate a swift response to minimize damage and contain the threat.
Except in the case of APTs, for instance, most malicious actors don’t need more than a few minutes to start causing damage to your systems once they are able to breach the network.
However, behavioral analytics systems, by correlating data from multiple sources and applying algorithms, can provide valuable insights and context to aid decision-making.
Insights From Incident Analyses
Ultimately, behavioral analytics should help you to enhance your overall security practices, starting with effective data detection and response. These systems collect and generate a wealth of data that can be used to gain a deeper understanding of attacker techniques and the effectiveness of existing security controls.
Subsequently, the insights can guide proactive measures such as fine-tuning access controls, implementing additional security layers, reconstructing attack timelines, etc.
The Future
This section focuses on future concerns and areas for development in applying behavioral analytics to data detection and response.
Increased Use of Machine Learning and AI
AI algorithms built on machine learning and neural networks are designed to self-improve. So, by integrating them into behavioral analytics for data security, one can only expect the technology to continuously advance its capabilities to learn from new data and adapt to evolving threats.
This will, in turn, enable more sophisticated anomaly detection, threat modeling, and predictive analytics. We must remember that even malicious actors are not resting on their oars; AI is now fully part of their attack techniques.
Security Integration
Behavioral analytics platforms are not designed to work in isolation. So, as adoption increases, we can hope to see tighter integration with technologies such as endpoint detection and response (EDR) and security information and event management (SIEM). Correlating behavioral insights with broader security events helps provide a 360-view of the threat landscape, something that seems unachievable with the numerous endpoints proliferating enterprise networks today.
Insider Threats
If there was a magic potion to stop all insider threats forever, many business leaders won’t hesitate. In the past few years, there has been a focus on employee education as a way to mitigate insider threats but now IT leaders are discovering that it is as much a technology problem as it is a people one. Therefore, behavioral analytics systems in the near future will place greater emphasis on detecting and mitigating insider threats and anomalies such as unauthorized data access, unusual data transfers, and attempts to bypass security controls, whether maliciously or inadvertently.
Conclusion
Behavioral analytics represents a paradigm shift in data detection and response, offering organizations a proactive and comprehensive approach to cybersecurity.
By analyzing patterns, behaviors, and anomalies within a system or network, behavioral analytics goes beyond traditional methods to detect emerging threats that may evade signature-based detection systems.
