Unlike signature-based detection, which uses predetermined rules to pinpoint known threats, UEBA tools analyze both user and entity behavior patterns. This allows them to detect anomalies and potential threats in their early stages, even before security analysts can identify them with traditional event data.
Integrating EDR, UEBA, and SIEM into one SOC platform can elevate your cybersecurity defenses.
1. Collect Data
UEBA systems collect data to establish a behavioral baseline that can detect deviations in activity. These can indicate potential security threats.
Unlike traditional SIEM solutions, platforms that centralize and unify broad functionality into one integrated platform, eliminate the need to switch between multiple tools with different consoles, languages, and datasets. This improves analyst efficiency and productivity.
They also accelerate threat detection and response, minimizing the window of vulnerability.
2. Automate Processes
In cybersecurity, detecting threats early is key. AI can help automate detection by identifying patterns and anomalies that human analysts might miss.
UEBA tools identify deviations from established behavioral baselines and flag potential threats, using dynamic risk scoring to prioritize incidents. The resulting threat-hunting and response workflows improve analyst productivity and efficiency while helping to protect the organization against evolving cyber threats.
3. Train Analysts
SOC analysts are like digital alchemists, transforming data into insights and threats into opportunities to strengthen defenses. However, they need the right tools and technologies to work effectively.
UEBA solutions can help them maximize productivity by identifying potential threats and attacks. This is accomplished by establishing baselines of normal behavior and identifying deviations from those patterns. This enables them to prioritize incidents and reduce the window of vulnerability.
4. Build a Data Science Team
Unlike traditional tools with siloed functionality, UEBA and AI-powered solutions are designed to integrate alerts and incidents across disparate security functions into one platform. This minimizes console switching and reduces engineering complexities.
Additionally, machine learning can identify patterns and anomalies humans do not easily spot. For example, it can detect both upward and downward shifts in activity to proactively identify threats that could otherwise be missed.
5. Define Use Cases
Clearly define objectives for integrating AI into your SOC. This will help determine the best AI technologies to use.
AI-driven UEBA tools provide a unified view of relevant log data and analysis, helping to reduce complexities and ambiguities. This helps to eliminate the need for slicing and dicing; and enables analysts to identify threats faster. This minimizes the risk of alert fatigue and improves analyst morale.
6. Choose the Right Technology
UEBA is a powerful tool that can help SOCs detect unknown threats. But it’s important to choose the right technology for your needs.
Ensure your solution is cloud-native to scale and supports multi-cloud environments. Look for a UI that provides a powerful, user-friendly way to create models and configure rules without needing data science expertise.
Several examples of UEBA solutions leverage ML to automatically identify and find abnormal behaviors for users and entities.
7. Integrate with Other Tools
The most successful SOCs leverage AI for UEBA and Anomaly Detection alongside other security tools to support comprehensive threat detection and incident response. This enables enhanced capacity, deeper investigations, accelerated response times, and improved analyst productivity.
A tool with conversational AI eliminates the need for slicing and dicing, pivots, and complex queries by allowing analysts to ask questions in plain language. It also understands behavioral context, reducing unnecessary alerts.
8. Automate Responses
Anomaly detection identifies deviations from established behavior baselines, flagging potential threats. Once a threat has been identified, automated responses can be initiated through SOAR integrations to mitigate risks.
Automated Response technology enables the modern SOC to improve security outcomes and transform the manual SecOps model with advanced machine intelligence and automation. Learn how this next-gen solution helps streamline operations, strengthen cybersecurity posture, and prepare for tomorrow’s attack methods.
9. Invest in Training
UEBA tools analyze historical behavior to establish baselines for normal activity, identifying deviations and anomalies that can indicate a security threat. This early warning helps SOC analysts respond before an attack reaches critical stages.
Some tools use the AI-UX value circuit—the same design pattern that powers text “completions” and error correction on touchscreen keyboards—to deliver an intelligent, automation-first platform for transforming SOC operations.
10. Automate Reporting
ML-based algorithms establish a baseline of normal behavior for users and entities, identifying anomalies that might indicate security threats. This reduces the window of vulnerability and allows SOC analysts to triage threat alerts based on actual risk.
Traditional SOCs depend on various tools for specific functions, resulting in disconnected data points and difficulty connecting alerts. AI-based solutions eliminate these siloes by unifying broad functionality into a single platform that operates on fully-integrated data.
While the process can seem daunting, the team at Asylum Technologies can help you make sense of the madness. Reach out today and let’s chat about what’s right for your business.
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