The rise of generative AI (Gen AI) is transforming industries worldwide, with tools like ChatGPT and advanced image and video generators opening up new possibilities across fields.
Cybersecurity is no exception, with Security Operations Centers (SOCs) increasingly leveraging AI to streamline operations. However, while Gen AI offers powerful capabilities, it’s not a magical silver-bullet. Effectively addressing modern SOC challenges requires a balanced approach that pairs AI with advanced tools, integrations, and skilled security professionals.
Let’s take a look at some of the strengths and weaknesses that security professionals should know about AI solutions built primarily on Large Language Models (LLMs).
Gen AI Strengths for Security Operations
Generative AI excels at specific SOC tasks, particularly those involving large volumes of text-based data, repetitive processes, and contextual analysis:
- Textual Artifact Analysis: Quickly interprets host-based alerts, command lines, PowerShell scripts, and emails, revealing intent and suspicious behavior.
- Pattern Recognition: Identifies subtle variations that humans might miss, such as a phishing email where the sender’s domain is slightly altered (e.g., replacing a capital “I” with a lowercase “L”).
- Generating Rules and Queries: Creates rules and queries from processed data to guide further investigations and threat-hunting.
- Summarizing Large Data Sets: Compiles detailed reports and summaries, allowing security teams to quickly extract key findings and take informed action.
Gen AI Weaknesses for Security Operations
No matter what anyone says, Gen AI simply can’t do it all. The technology has clear limitations that prevent it from fully automating SOC operations:
- Critical Thinking: Lacks the deep thinking required for high-stakes decisions and is quite gullible by design.
- Raw Evidence Collection: Cannot actively collect logs, memory images, or endpoint data directly from security tools or systems.
- Non-Text-Based Analysis: Tasks like reverse engineering binaries and analyzing network artifacts are beyond AI’s capabilities.
- Operative Actions: Cannot engage with users, enforce security policies, or take actions like revoking access.
- Sandbox File Analysis: Cannot execute files in a sandbox environment to observe behavior.
- IP and Browsing Analysis: Requires dedicated services to determine if an IP is malicious and identify abnormal behaviors.
A Hybrid AI SOC Approach
The fact is, many AI SOC solutions fail because they over-rely on Gen AI, expecting it to handle every aspect of SOC automation. This leads to incomplete investigations, missed threats, and unreliable results.
Successful SOC automation must combine AI-driven intelligence with deterministic analysis and broad integrations.
Here’s how all the pieces come together in the autonomous SOC:
- Detection: Dedicated integrations and APIs receive alerts from endpoints, networks, email, and identity systems.
- Investigation: AI analyzes textual evidence (i.e. command lines, scripts, and emails), while deterministic tools handle non-text evidence like binary files and IP reputation checks. AI then correlates results to build a comprehensive threat picture.
- Decision-Making: AI aggregates and interprets all the results from various analysis and AI engines, providing a final verdict, classification, and priority level. This ensures that only critical alerts are escalated for human review.
- Response: While AI can generate hunting queries to find additional related cases, operational actions like closing false positives or blocking IOCs require dedicated integrations with security tools.
- Reporting: AI compiles investigation summaries, root cause analyses, and recommended actions.
How Intezer’s Hybrid Approach Stands Out
Intezer’s Autonomous SOC pairs our private instances of leading LLMs with forensic engines, automated analysis, and extensive integrations to architect an industry-leading solution that delivers accurate, actionable insights.
- Forensic and Security Engines: Gather endpoint artifacts, perform deep memory forensics, and reverse engineer binaries to provide concrete evidence.
- Deterministic Analysis: Ensure that AI-driven decisions are based on structured, verifiable evidence rather than assumptions.
- Broad and Seamless Integrations: Connect with SIEMs, SOARs, EDRs, and phishing pipelines to access real-time data from security systems.
By leveraging Gen AI where it’s most effective and using dedicated solutions for tasks AI can’t handle, SOC teams can achieve faster, more effective threat detection and response.
See Intezer’s Autonomous SOC in Action. Request a demo today.
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