Key Elements of Soc Transformation

How Do You Measure the Impact of an Agentic AI SOC Analyst? 🤔 Agentic AI is transforming Security Operations Centers (SOCs) by addressing critical challenges such as alert fatigue, high costs, and low morale. But how do organizations measure its impact on their security operations? Here’s how customers are answering this question for their teams, executives, and boards: 1. Efficiency: Saving Time ⏱️ Agentic AI eliminates manual, repetitive tasks like triaging and investigating alerts. This leads to faster investigations and reduced Mean Time to Respond (MTTR). By automating these processes, SOC teams can focus on higher-value tasks such as threat hunting. 2. Risk Reduction: No Alerts Ignored 🛡️ AI SOC Analysts investigate every alert—whether low, medium, or high severity—within minutes. This comprehensive approach ensures no potential threat goes unnoticed and reduces dwell time, minimizing the impact of security incidents. 3. Reduced Costs: Doing More with Less 💸 Organizations can achieve greater operational efficiency without increasing headcount. By automation and streamlining workflows, Agentic AI reduces the cost of running a SOC while improving overall security posture. 4. Improved Morale: Retaining Talent 😊 Alert fatigue and monotonous tasks often lead to burnout among SOC analysts. Agentic AI alleviates this by handling routine tasks, allowing analysts to focus on engaging and strategic work. This boosts job satisfaction and accelerates career growth for junior analysts. 5. Higher Impact: Strategic Focus 🔍 By eliminating manual tasks, Agentic AI enables SOC teams to concentrate on complex investigations and proactive security initiatives. This shift not only improves operational efficiency but also enhances the overall effectiveness of the security team. Agentic AI augments and empowers SOC teams to work smarter, faster, and more effectively.  By measuring success across efficiency, risk reduction, cost savings, morale improvements, and strategic impact, organizations can clearly demonstrate the value of integrating AI into their security operations.

Great conversation with Kumar Saurabh on AI in SOC. Kumar Saurabh is the OG having built SIEM twice (ArcSight and SumoLogic) and SOAR (LogicHub) Video link in comments: The State of SOCs and the Talent Gap Kumar highlights the cybersecurity talent shortage, emphasizing the need for high-quality personnel rather than just increasing headcount. Traditional SOCs are structured into Tier 1 (entry-level), Tier 2, and Tier 3, with expertise increasing at each level. The challenge lies in handling the growing volume and complexity of security alerts while maintaining cost efficiency. AI Analyst for Tier 1 Automation Kumar argues that AI can fully replace human analysts in Tier 1 roles, citing successful deployments in production. He references a blind A/B test where AI outperformed human analysts in quality, speed, and cost. While Tier 2 and Tier 3 still require human oversight, AI significantly reduces their workload, allowing experts to focus on complex cases. Shifting SOC Structure AirMDR has adopted an AI-first SOC structure, where AI handles all Tier 1 tasks, Tier 2 analysts supervise AI, and Tier 3 experts refine the AI’s performance. This feedback loop ensures continuous improvement. The AI-Driven Alert Analysis Process Kumar outlines a three-stage process for AI-driven alert handling: Enrichment – Collecting contextual data (IP ownership, user roles, etc.). Decision-Making – Assessing whether an event is malicious, benign, or suspicious. Response – Taking automated or recommended actions. LLMs: System One vs. System Two Thinking Kumar differentiates between "System One" (fast, intuitive thinking) and "System Two" (deep, analytical reasoning). LLMs excel at System One tasks, making them suitable for structured decision-making but less effective at complex investigations. He advocates for a hybrid approach, combining LLMs with human expertise for higher-level reasoning. Dynamic Playbook Generation vs. SOAR Limitations Traditional SOAR platforms rely on rigid, pre-defined playbooks that lack adaptability. Kumar argues that LLMs enable dynamic, real-time playbook generation, making AI-driven SOCs more flexible and cost-effective. Threat Intelligence & Data Enrichment LLMs assist in analyzing threat intelligence reports and extracting useful insights. The key is formulating the right questions to maximize their reasoning capabilities. Data Placement & Edge Analytics Kumar suggests a hybrid analytics approach: simple filtering should be handled at the edge, while complex analysis (e.g., User and Entity Behavior Analytics) requires centralized processing. The Future of SOCs: Leaner & AI-Driven Kumar predicts that within 3-5 years, AI will dominate Tier 1 SOC operations, drastically reducing costs and improving efficiency. SOCs will become leaner, with human analysts focusing on AI supervision and complex security incidents.

For the first time in history, the #1 hacker in the US is AI …but as the threats have been evolving, so have the solutions. Over the past year, the focus for all major players has shifted to building an AI-enhanced SOC (Security Operations Center). Every company has a different approach, but the key trend has been building out data infrastructure and response capabilities on top of the data that companies already have. Here are the key components of the Agentic AI SOC. ◾ Sources of Data ◾Data Infrastructure ◾Response and Decision Layer ◾AI Agents that act on these insights While the ultimate goal is to create AI Agents, that is not necessarily where the value lies. Companies were able to whip up AI Agents shortly after the first LLMs were introduced. I think the value will be in the data, both the Source and the Data Infrastructure Layer. 1. Sources of Data. This stems from a large installed customer base. Here, leaders in Network, Endpoint, Identity, and Cloud security have a significant advantage, as they already possess large amounts of data. 2. Data Infrastructure: This is an emerging area where there is ample room for new entrants to offer innovative solutions. It is also the primary source of acquisitions for large, publicly traded companies. As Francis Odum from Software Analyst Cyber Research put it “We know that data sources are multiplying rapidly with GenAI. More tools mean> more data sent into SIEMs > which means more storage, costs, and alert noise! If we solve issues at the data sources (filter, normalize, threat intel enrichment, and importantly, fix detection rules, etc.), everything else will follow. In the next phase of cybersecurity, the winners will be those who can move from collecting data to orchestrating outcomes and build cohesive platforms. Where do the public players stand today? 🟩 Companies that are building unique platforms are winning: Zscaler, Cloudflare, CrowdStrike, Palo Alto Networks 🟥 Companies that rely on antiquated technologies are losing: Splunk, Exabeam We just published Spear 's updated Cybersecurity Primer, which delves into recent cybersecurity trends and provides a lay of the cybersecurity landscape. You can access it here: https://lnkd.in/gWdRfxnz #cybersecurity #ai #technology