Source: www.govinfosecurity.com – Author:
Artificial Intelligence & Machine Learning , Big Data Security Analytics , Next-Generation Technologies & Secure Development
Joe DeFever • October 15, 2024
By collecting, analyzing, and leveraging data from security events, security analytics empowers teams to proactively detect anomalies and pinpoint vulnerabilities to mitigate targeted attacks, insider threats, and advanced persistent threats (APTs).
See Also: Live Webinar | SOC Monitoring: Around-the-Clock Threat Protection
Now, with generative AI (GenAI) integrated into the various tools of a security analytics platform, security teams are even further elevated through:
- Predictive analytics:
You can parse through historical data to identify attack patterns or potential risks and even proactively forecast cyber attacks.
Establish a comprehensive understanding of routine activities, then monitor and correlate various data points (e.g., user behavior, host activity, network traffic) to identify anomalies that indicate malicious activity.
Streamline alert triage, report generation, and decision-making for the next best actions, without missing a beat.
AI-driven security analytics improves your security team’s ability to respond to incidents much faster and maintain a more robust defense posture.
Consider these security analytics benefits and how they could level-up your defense efforts:
- Enhanced visibility:
Gain holistic visibility across your enterprise systems, networks, endpoints, apps, and other assets so that notable events aren’t slipping through the cracks.
Identify the most critical vulnerabilities and risks before they can have a major impact.
Detect cyber attacks with high-fidelity, automated alerts and respond to them with AI-enhanced context and recommendations.
Gain stronger insight into your IT environment to help you understand where to allocate resources so you can further strengthen your security posture.
Building a successful security analytics program involves a combination of tools. While the list below is by no means exhaustive, we’ll explore the essentials:
1. Security information and event management (SIEM)
SIEM tools collect and analyze security data across your network. With the new generation of AI-driven SIEMs comes advanced analytics, which expose unknown threats and provide a sharpened assessment of risk across your infrastructure.
SIEM combines log management with threat hunting and incident response capabilities, enabling you to eliminate blind spots, strengthen defenses, and streamline workflows.
2. Extended detection and response (XDR)
XDR correlates data from various sources across your IT environment — endpoints, network, access management, cloud, and more — and detects and responds to cyber threats across it.
With XDR, visibility is key. When you apply XDR’s detection, investigation, and response capabilities to gather and analyze data across your ecosystem, your teams can uncover and disrupt complex attacks, at scale.
3. Cloud security
Cloud security ensures data integrity and compliance across cloud and container environments — it becomes an especially powerful solution when equipped with GenAI capabilities, which proactively combat cloud-based threats, secure workloads, and strengthen overall security posture.
Within the broader framework of AI-driven security analytics, a cloud security solution prioritizes cloud configuration risks — whether from native or third-party sources — to help you focus on the most critical issues for improved security posture and compliance.
4. Security orchestration, automation, and response (SOAR)
SOAR tools automate and streamline security tool workflows and incident response processes for your team, improving overall efficiency. When integrated across the tools in your security analytics stack, SOAR saves your team major time. When it’s enhanced with GenAI, its automation capabilities are multiplied — adapting more tailored and effective alerting and response workflows.
5. User and entity behavior analytics (UEBA)
UEBA analyzes user and entity data types (e.g., user activity logs, access patterns, device information) within your system to establish a benchmark of relative normalcy, and then it alerts when anomalous activity occurs.
UEBA tools are effective at identifying insider threats where other technologies are likely to miss, making it an essential component for successful security analytics.
6. Threat intelligence platform (TIP)
A TIP aggregates, analyzes, and actions upon threat intelligence data sourced from various threat intelligence feeds. It’s essential for layering in context around alerts and incidents and provides up-to-the-minute defense against the latest threats and vulnerabilities.
Automated threat classification comes courtesy of GenAI, which, through dynamic tagging, applies tags and categories to threats in real time — helping security teams prioritize and respond effectively.
Today’s threats are becoming increasingly dynamic, further compounded by adversaries’ increasing use of GenAI. Now more than ever, organizations need a holistic approach to cybersecurity — one that requires a broader spectrum of visibility and protection than any one security solution can provide.
Security analytics taps into the strengths of each of the tools in its stack — all while keeping those systems unified so that teams don’t fall into silos. Just as each member of your security team brings unique expertise, abilities, and perspective to the table, so too do these top security analytics components.
With each of these tools operating on the same data set and GenAI supplementing your workflows, your team will be equipped to take on today’s top security challenges.
Learn more about the latest evolution of AI-driven security analytics.
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Original Post URL: https://www.govinfosecurity.com/blogs/essential-tools-for-building-successful-security-analytics-p-3737
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