# Agentic AI in SOC: Building an Autonomous Security Operations Center - By [Sarah Breathnach](/content/en/blog/author/sarah-breathnach/index.html) 19 minutes to read ## Table of Contents - [What Is Agentic AI in Cybersecurity?](/content/en/blog/agentic-ai-secops#title0/index.html) - [Key Insights: Why Should SOC Teams Care About Agentic AI](/content/en/blog/agentic-ai-secops#title2/index.html) - [Rethinking the AI SIEM and AI SOC](/content/en/blog/agentic-ai-secops#title3/index.html) Under constant pressure to do more with less, security operations teams are turning to AI as the long-promised solution. But here’s the catch: most AI offerings today don’t solve the problem - they just repackage it. Despite all the hype around “AI-SOC analysts," many tools stop at co-pilot functionality: summarizing alerts, translating queries, or responding to prompts. Helpful? Sometimes. Transformative? Not even close. These systems still wait for human direction. They don’t think, decide, or act on their own. That’s where [agentic AI](/content/pathfinder-ai/index.html) comes in. Agentic AI represents a fundamental shift. AI that operates with autonomy, builds and executes investigation plans, draws conclusions, and initiates response plans without human intervention. It's not here to assist. It’s here to investigate. In this post, we’ll explore how agentic AI is reshaping cybersecurity operations - what it is, why it matters now, and how it addresses the very challenges today’s co-pilot tools can’t. ## What Is Agentic AI in Cybersecurity? [Agentic AI](/content/pathfinder-ai/index.html) refers to artificial intelligence systems that not only assist but take autonomous action. In the SOC, this means independently conducting investigations, correlating evidence across multiple telemetry sources, and even making triage decisions with minimal human input. Unlike reactive AI tools - which are limited to summarizing alerts, answering prompts, or translating queries - agentic AI behaves like a skilled analyst. It doesn’t wait for direction. It proactively investigates threats, determines intent, and drives outcomes. ### How Agentic AI Differs from Reactive AI | | Reactive AI (Co-pilots) | Agentic AI | | --- | --- | --- | | Requires prompts | Yes | No | | Investigates threats | Limited or more | Full, multi-step investigation | | Acts independently | No | Yes | | Updates its plan | No | Iterative, context-driven decisions | | Recommends remediation | Rarely | Tailored, just-in-time playbooks | ### What Agentic AI Systems Actually Do Agentic AI systems in cybersecurity: - Receive alerts without being prompted - Build dynamic, tailored investigation plans using domain knowledge - Deploy domain-specific agents (e.g., cloud, identity, endpoint) - Run automated enrichments and data queries - Adapt investigation paths based on new findings - Classify threats as malicious, benign, or inconclusive - Generate actionable response plans or tuning suggestions - Explain every step taken, including dead ends and decisions It’s not about layering AI on top of existing tools for superficial value. Instead, it’s about designing a system that understands the full lifecycle of an alert - from signal to conclusion - and treats investigation as a structured, iterative process. Rather than relying on an analyst to manually guide the AI at every step, the [modern SIEM](/content/site-root.html) should drive the investigation on its own, escalating only when needed and always explaining what it did and why. > _“We aren’t just taking an LLM and throwing it at an alert. We build a full investigation plan, deploy agents, and draw a conclusion—just like an analyst would.”_ > _— Ian Forrest, VP of Product, Hunters_ For SOC teams inundated with noise and repetitive tasks, this approach doesn’t just save time - it redefines how investigations are handled altogether. ## Key Insights: Why Should SOC Teams Care About Agentic AI It’s Not Just a Co-Pilot. Many AI tools in cybersecurity today are designed as co-pilots - they assist analysts by summarizing alerts, translating queries, or generating basic insights. But they depend on constant human prompting. [Agentic AI](/content/pathfinder-ai/index.html) goes far beyond that. It operates without waiting for direction, driving investigations end-to-end. [Agentic AI](/content/pathfinder-ai/index.html) doesn’t just answer the “what.” It uncovers the “why,” “how,” and “what next.” In a real-world SOC, investigations are often broken down across team members with domain-specific expertise - one analyst might handle cloud environments, another might focus on endpoints, and so on. [Get a demo](/content/watch-a-demo-hunters-soc-platform/index.html) to see the impact Agentic AI can have on your SOC. [Agentic AI](/content/pathfinder-ai/index.html) reflects that model by deploying domain-specific agents: - Cloud agents investigate infrastructure like Snowflake, AWS or Azure - Identity agents dig into user behavior and access patterns - Endpoint agents trace malware behavior - Network agents analyze flow logs and anomalies Each agent has purpose-built tools and logic, ensuring investigations are precise and contextually relevant. > “These aren’t general LLM prompts. These are field-tested tools built to investigate at depth—and they now power our agents.” > _— Yuval Zacharia, Director of AI & Security Research, Hunters_ ### Autonomous Decision-Making Loops [Agentic AI](/content/pathfinder-ai/index.html) doesn’t follow a fixed script. As it investigates, it evaluates whether it has enough evidence. If not, it updates its investigation plan, pivots on new findings, and continues. This mirrors how human analysts iterate - following clues, discarding dead ends, and drilling deeper when needed. ### Transparency Over Abstraction One major challenge with traditional AI models is a lack of visibility into their decision-making. [Agentic AI](/content/pathfinder-ai/index.html) changes that. Every step of the investigation is documented, auditable, and traceable. Transparency Features: - Investigation steps are logged in order - Dead ends are shown, not hidden - Data used for each decision is surfaced - Reasoning behind each conclusion is included - Analysts can review, validate, or override conclusions > “We show everything—even the false leads—so the analyst can validate and understand what the AI did.” > _— Ian Forrest, VP of Product, Hunters_ ### From Response Plans to Tuning Suggestions After an alert is investigated, a [gentic AI](/content/pathfinder-ai/index.html) doesn’t just stop at a label. It takes the next step - generating bespoke response plans or tuning recommendations based on its findings. Outcome Scenarios: - Malicious finding: Generate a tailored remediation plan - Benign finding: Recommend a tuning rule to suppress future noise - Inconclusive: Suggest data sources or follow-up steps for the analyst ### Avoiding Hallucinations with Purpose-Built Tools Generic LLMs often hallucinate, producing confident but inaccurate results. [Agentic AI](/content/pathfinder-ai/index.html) avoids this by relying on a library of over 200 atomic, researcher-developed tools for enrichment, investigation, and evidence gathering. These tools are used by agents for precise tasks, reducing reliance on generic language model reasoning. > “We built these tools first for our automatic investigation engine. Now our agents use them—reducing hallucinations and improving signal-to-noise.” > — _Yuval Zacharia, Director of AI & Security Research at Hunters_ ### Self-Tuning Detection Logic Agentic AI systems are designed to scale across diverse environments without needing extensive manual tuning. Detection logic is built to self-optimize using signals from each customer’s infrastructure. > “Each rule needs to work out-of-the-box across hundreds of different environments. We build them to be that robust from day one.” > — _Yuval Zacharia, Director of AI & Security Research at Hunters_ ### Faster Deployment, Shorter Feedback Loops Instead of the traditional 6–12 month [SIEM](/content/site-root.html) rollout, an AI-driven platform like Hunters is operational in weeks. With native automation, built-in detection content, and cloud-native architecture, time-to-value is dramatically reduced. > “We’ve taken deployments that used to take quarters and made them deliver results in weeks.” > — _Ian Forrest, VP of Product, Hunters_ ### Privacy and Compliance Built-In, Not Bolted On Security and privacy aren’t bolted on - they’re part of the core design. Agentic AI capabilities in Hunters include safeguards to meet compliance and customer isolation standards. Privacy Features: - Customer environments are fully isolated - AI models run in inference-only mode (no training on customer data) - EU-based hosting available to meet GDPR requirements > “Privacy was built into the architecture. Not bolted on later.”— _Yuval Zacharia, Director of AI & Security Research at Hunters_ ### Elevating Analysts, Not Replacing Them Agentic AI is not about removing humans from the loop (HITL) - it’s about removing manual, repetitive tasks from their plates. Analysts shift from conducting investigations to reviewing and validating AI-driven ones. This shift improves speed, accuracy, and morale. > “We’re not replacing the analyst. We’re upgrading their workflow.” > — _Ian Forrest, VP of Product, Hunters_ ## Rethinking the AI SIEM and AI SOC Not All AI Is Created Equal AI is now a standard claim in security products. But what that AI actually does varies widely. Some solutions enhance the UI, others assist with basic summarization, but very few deliver operational autonomy. > “Most AI solutions today are copilots. They help when asked. They don’t initiate, investigate, or conclude.” _— Ian Forrest, VP of Product, Hunters_ This is the core distinction: reactive assistance vs. agentic autonomy. Understanding that difference is key to distinguishing between hype and meaningful innovation. ### Two Paths: Co-Pilots vs. Agentic AI [Agentic AI](/content/pathfinder-ai/index.html) goes far beyond the reactive AI most SOCs encounter. Here’s how they compare: > “We deploy specialized agents that mimic how a real SOC team operates—each one focused, accountable, and built to reason.” — _Yuval Zacharia, Director of AI & Security Research at Hunters_ Source: Hunters.ai ### Real-World Deployments Are Happening Now [Agentic AI](/content/pathfinder-ai/index.html) isn’t a lab experiment - it’s operational today in live environments. Organizations are using agentic platforms to: - Offload triage and enrichment - Investigate alerts across cloud, endpoint, and identity - Automatically surface validated, actionable conclusions - Streamline analyst workflows through transparency and oversight > “We’ve taken deployments that used to take quarters and made them deliver results in weeks.” > — _Ian Forrest, VP of Product, Hunters_ This accelerated time-to-value is driven by cloud-native architecture and structured detection logic that requires minimal manual tuning. [Get a demo](/content/watch-a-demo-hunters-soc-platform/index.html) to see this for yourself. ### Benefits of Agentic AI in the SOC Teams that implement [agentic AI](/content/pathfinder-ai/index.html) see immediate, tangible benefits: .png?width=1200&height=800&name=benefits%20of%20agentic%20AI%20in%20the%20soc%20(1).png) Source: Hunters.ai ### A Shift in SOC Operations Adopting [agentic AI](/content/pathfinder-ai/index.html) is more than adding a new tool - it’s a structural shift in how investigations happen. > “Each step is logged, each decision documented. It’s how we give analysts trust without making them repeat the work. We're not replacing the analyst. We’re upgrading their workflow.” — _Ian Forrest, VP of Product, Hunters_ The challenge is no longer about whether you need AI - it’s about what kind of AI you need. Reactive copilots and chatbot interfaces may save minutes, but they rarely shift outcomes. [Agentic AI](/content/pathfinder-ai/index.html), on the other hand, has the potential to restructure how your SOC operates. > “This isn’t just an overlay. It’s a fundamentally different architecture—one built to think, act, and adapt like an analyst.” _— Yuval Zacharia, Director of AI & Security Research_ Before choosing your next [SIEM](/content/site-root.html) or evaluating a new AI solution, ask: Does this platform act with autonomy, or does it still wait for me to tell it what to do? That one question will help you distinguish between incremental improvements and transformational change. ### Comparison: Legacy/Traditional SIEM vs. Next-Gen | | Legacy SIEM (Traditional + Co-Pilot AI) | Next-Gen (Agentic AI SIEM) | | --- | --- | --- | | AI Role | Passive assistant | Autonomous investigator | | Prompt Dependency | High (requires human input) | Low (initiates tasks independently) | | Investigation Depth | Shallow (summarization, query help) | Deep, multi-stage, multi-agent | | Remediation Support | Manual or semi-automated | Just-in-time playbooks generated by AI | | Transparency | Limited reasoning visibility | Full audit trail of investigation logic | | Tuning & Noise Suppression | Reactive, manual adjustments | Proactive, AI-suggested tuning plans | | Time to Deploy | Months to operationalize | Weeks to full value | | Analyst Role | Hands-on investigator | Strategic reviewer and decision-maker | [Agentic AI](/content/en/blog/pathfinder-ai-part-1/index.html) isn’t a buzzword - it’s a practical shift in how we defend against real threats at scale. And it’s already here, changing the day-to-day lives of security teams. If you're evaluating your next SOC investment, don’t just look for AI in isolation. While many vendors attempt to build agentic functionality separately, doing so without a deeply integrated SIEM limits effectiveness: > “There are a number of different companies that are trying to build agentic AI, and they’re trying to build it independently… It takes a lot of effort to collect all the data, to normalize all the data, understand all the data… and build up a follow-up investigation plan.” — _Ian Forrest, VP of Product, Hunters_ Don’t settle for siloed copilots or disconnected AI claims. See how a Hunters AI-driven SIEM can transform your investigation workflow - start to finish. ## Frequently Asked Questions (FAQ's) About Agentic AI in SOC **What's the difference between traditional AI and agentic AI in security operations?** Traditional AI systems in security are typically single-purpose tools that perform specific tasks like anomaly detection or log analysis. Agentic AI represents a more advanced approach where multiple AI agents work together, each with specialized capabilities, to autonomously handle complete security workflows from detection through remediation. **How long does it typically take to implement a SIEM with agentic AI capabilities?** Implementation timelines vary based on environment complexity, but most organizations can expect 4-12 weeks for initial deployment and another 4-8 weeks for optimization and training. Cloud-native environments may see faster implementation cycles. **What skills do SOC analysts need when working with agentic AI?** Rather than being replaced, analysts' roles evolve to focus on governance, training the AI system, handling edge cases, and strategic threat hunting. Key skills shift toward AI supervision, pattern recognition, and higher-level security strategy.