Agentic AI SOCs differ from copilot-only models by autonomously prioritizing attacks over alerts, executing closed-loop containment, and providing traceable reasoning for every decision, allowing analysts to focus on high-value investigations.
The shift from AI-assisted tooling to agentic, AI-native security operations is no longer theoretical. It is entering production at scale, and 2026 represents the practical inflection point for enterprise SOCs. Agent frameworks are stabilizing, defenses against agent-specific attacks are maturing, and executive stakeholders increasingly demand AI-driven outcomes that are transparent, explainable, and auditable.[1]
Nearly two-thirds of organizations are already experimenting with AI agents, yet fewer than one in four have deployed them into production. That gap signals a transition moment. As governance models, architecture standards, and risk controls mature through 2026, adoption is expected to accelerate rapidly. At the same time, the market for agentic capabilities is projected to grow sharply through 2030, underscoring that this is not a short-term trend but a structural transformation.[2]
Taken together, these signals make 2026 the year to move from pilot to platform. The operational payoff is clear: faster triage, more precise investigations, and automated response that prioritizes attacks over alerts, explains decisions with evidence, and scales safely under real-world enterprise constraints.
The Rise of Agentic AI in Security Operations
Agentic AI refers to systems that can plan, act, and adapt without step-by-step human guidance. These systems use evolving context, often coordinate multiple agents to solve complex problems, and can perceive their environment, reason about what they observe, plan a sequence of actions, and execute them to achieve specific goals without human intervention, while leveraging the tools assigned to them.
In a Security Operations Center (SOC), the team responsible for monitoring, detecting, and responding to cyber threats, agentic AI enables agents to gather context, analyze signals, take controlled actions, and learn from each outcome across triage, investigation, and response.
What began as “copilots” helping SOC analysts write queries is now evolving into autonomous systems capable of reasoning, acting, and adapting across complex investigations.
An agentic AI SOC differs from a traditional “copilot-only” SOC in three key ways:
Prioritization: Correlates multi-modal telemetry and adversary intent to identify complete attack chains rather than isolated alerts.
Closed Loops: Moves beyond detection into containment, executing automated workflows and leveraging safe tool access to resolve threats at machine speed.
Transparency: Provides traceable context and citations for every action, allowing SOC analysts to verify, trust, and override decisions. Without this, an agentic SOC would be a "black box," making it impossible for analysts to verify, trust, or safely override decisions.
By automating routine enrichment and research tasks, correlating alerts into meaningful attack chains, and executing safe response actions, agentic AI enables SOC analysts to focus on high-value investigations while maintaining full visibility and control.
Key Drivers Behind the Agentic AI Inflection Point
Three forces are driving the transition to agentic AI SOCs:
Scaling and standardization pressure: Many SOCs have experimented with AI agents but lack mature production practices. Leaders are enforcing architecture standards, governance controls, and operational policies to move beyond pilots.
Escalating threat landscape: Attackers are using stealthier, multi-stage techniques, often AI-enhanced or even AI-created, that blend into legitimate activity and move faster than manual workflows can handle. SOCs must adopt autonomous, goal-driven systems to continuously correlate signals and respond at scale without losing control.
Maturing ecosystem: Agentic attacks and defenses are evolving in parallel, creating demand for new SOC tooling, multi-agent visibility, and operational guardrails for safe, scalable deployment.
These drivers make adopting an agentic AI SOC both operationally and economically compelling, enabling faster triage, more precise investigations, and automated response. Analysts can focus on validated, correlated attack activity instead of individual noisy alerts, while decisions remain evidence-based and transparent, allowing organizations to scale safely under real-world constraints.
Operationalizing an Agentic SOC: Challenges and Recommendations
Scaling autonomous AI agents across an enterprise SOC introduces operational, governance, and economic challenges. Below are key challenges and recommended approaches to address them:
Challenge | Recommendation |
Early automation efforts target low-impact or low-noise tasks | Focus on high-volume, repetitive tasks such as risky LOLBins or failed logins, where automation delivers immediate ROI and reduces analyst workload. |
Agents performing actions outside their intended scope | Treat agents as Non-Human Identities (NHIs), enforce least-privilege access to tools, and enforce requiring human approval for high-impact actions. |
Agents behaving inconsistently or unpredictably | Treat prompts as code: version-control and rigorously test system prompts to ensure repeatable and reliable performance. |
Overloading a single agent or fragmenting the SOC with multiple domain-specific agents | Deploy a unified agent that dynamically loads task-specific instructions and tools on demand, keeping the core system lightweight. |
SOC analysts unsure of or unable to trust autonomous decisions | Prioritize explainability with RAG and transparent reasoning traces so every autonomous step is verifiable and grounded in evidence. |
Costs growing uncontrollably as agent deployment scales | Implement per-agent budgets, rate limits, and usage monitoring to manage token consumption and tool invocation expenses. |
Bloated system prompts increasing token costs and reducing agent accuracy. | Adopt an architecture where the agent pulls in targeted behavioral packages only when triggered by specific analyst intents or data context. |
Agents or automation workflows being exploited by attackers | Continuously test defenses via red-team exercises against agents and prompts to proactively identify and remediate vulnerabilities such as prompt injection. |
The Elastic Blueprint: Essential Capabilities for an Agentic SOC
To move from manual intervention to an autonomous "agentic loop," an enterprise-ready SOC must deliver measurable improvements across the entire triage -> investigation -> response lifecycle.
The following table outlines the essential elements of an agentic SOC platform and how Elastic Security operationalizes them:
Elements | What "Good" Looks Like in an Agentic SOC | How Elastic Supports |
Enterprise Scalability | Continuously reason across hybrid-cloud and on-premises telemetry, scaling autonomous threat detection and response across large, distributed enterprises. | Built on the Elasticsearch and AI Platform, Elastic Security provides unified visibility by ingesting data from any source—including cloud, identity, and endpoint. By consolidating all telemetry into a single platform that deploys natively on-premises, in the cloud, or in air-gapped environments without moving data, agents gain the broad visibility they need to reason across domains. |
Attack Prioritization | Prioritizing attacks over alerts by correlating signals to identify high-risk campaigns. | Elastic Attack Discovery is an agentic workflow that retrieves relevant alerts and correlates them into unified attack chains. But it goes beyond simple alert correlation. Using available security skills, it hunts for related activity, analyzes individual alerts, examines entity context, and searches raw logs across the Elasticsearch platform to gather supporting evidence before generating a prioritized attack.The result is a richer, evidence-grounded view of the attack, giving analysts a much stronger starting point for investigation. |
Accurate Detection | Faster and more accurate threat detection using behavioral baselines rather than static signatures. | Elastic Security Labs provides expert-driven detection rules for emerging threats, while Elastic XDR stops attacks across endpoints and clouds. This defense leverages Elastic’s machine learning and entity analytics to detect behavioral anomalies beyond static signatures. It monitors user and host activity, correlates events across systems, and uses endpoint behavioral analysis to identify suspicious patterns in real time. |
Custom agent builder | Agents operate toward defined objectives with multi-step reasoning and controlled tool access. | Elastic Agent Builder Build custom AI agents by connecting ES|QL for analysis, Elastic Workflows for deterministic actions, and context engineering — so agents can run complex tasks on their own. The platform includes composable AI skills for triage, hunting, detection, entity analytics, and investigation, and they can call each other to work through multi-stage threats. The MCP app brings these same capabilities into Claude, VS Code, and Cursor. |
Incident Response orchestration | Predictable execution for known scenarios, adaptive reasoning for complex ones, with analyst control at every stage. | Elastic Workflows handle the deterministic orchestration of triggers, sequencing, and response actions, while Agent Builder manages the AI reasoning. Seamlessly integrated, agents can call Workflows through conversations and Workflows can call Agents during orchestration. Human-in-the-loop controls ensure every automated step is backed by traceable evidence, allowing SOC analysts to override the system at any point. |
Flexible LLM Integration | A platform that supports your choice of LLM to avoid vendor lock-in and optimize for cost or privacy. | Elastic offers choice and control by letting you bring your own LLM. You can use OpenAI, Amazon Bedrock, Google Gemini, or local models to drive autonomous reasoning while maintaining full data sovereignty. For customers who prefer a turnkey experience, Elastic provides managed LLMs out of the box, ensuring that the power of an agentic SOC is accessible regardless of your preferred infrastructure. |
Transparent Reasoning | Explanations with clear evidence trails and source links. | In Elastic, agent reasoning provides a transparent trace of all tools used and decisions made, giving full visibility into the agent’s logic, while RAG (Retrieval-Augmented Generation) ensures every investigation is grounded in your organization’s internal knowledge, linked evidence, and includes source citations. |
Guarded autonomy | Explicitly permitted tools, confidence thresholds, RBAC, and controlled response scope. | Elastic lets you control the level of autonomy for your agents by managing assigned tools, skills, alongside user- and API-level permissions and RBAC. |
How Elastic’s Agentic AI Automates the LOLBins Hunt
It’s 9:15 AM. Your SOC dashboard shows zero "Critical" alerts, yet low-priority telemetry is flooding in. Among this noise, a stealthy process is running certutil.exe
to download a base64-encoded payload from a suspicious domain. LOLBins, or Living off the Land Binaries, are legitimate system tools such as certutil.exe or powershell.exe that attackers weaponize. Because these tools are trusted and digitally signed, their malicious use often blends into normal activity and goes unnoticed.
In a traditional SOC, this activity would not trigger an immediate response. Instead, it would likely remain hidden until a separate catastrophic event - such as an appearance of a ransomware note - forced a manual hunt. An analyst would then have to painstakingly backtrack, sifting through proxy logs, running complex queries, and manually decoding strings to confirm that certutil.exe had been weaponized. By that time, the attacker has usually already achieved their objective.
In an Agentic SOC, the work is already done. The agent has detected, enriched, and confirmed the threat, created a case, and sent notifications, all before you’ve even had your coffee.
Let’s see how it’s done with Elastic.
Detection: Uncovering Hidden Threats
Elastic's Attack Discovery correlates multiple alerts to reveal a complete attack narrative. When certutil.exe
executes in an unusual context, detection rules generate alerts, which Attack Discovery links with the originating phishing email and any related telemetry. The result is a unified story that shows not only the certutil.exe execution but also what the attacker attempted, how the payload was delivered, and the full sequence of malicious activity across the environment.
Autonomous Enrichment: Gathering the Evidence
Elastic Workflows can invoke agents on a schedule (ex: nightly threat hunts) or in response to events (ex: a new Attack Discovery finding) to operate automatically and gather evidence without human intervention.
When invoked, the agent investigates suspicious activity by analyzing file paths to identify malicious files, querying DNS logs to determine the IP resolution for the command-and-control domain, and searching firewall logs across clusters using ES|QL, Elastic’s piped query language, to confirm whether the traffic is allowed. This automated process allows the agent to collect and correlate critical signals across the environment without manual effort.
Every interaction with the agent is captured in a reasoning trace, recording each step the agent takes, including queries run, tools used, and enrichment results. This provides full transparency and auditability, and within the Agent Builder UI, SOC analysts can view these traces for complete visibility into how the agent reached its conclusions, the actions it performed, and the evidence it collected.
The screenshot below shows the reasoning trace of the agent and the tools it used during this investigation.
Verdict & Reasoning: Confirming the Threat
The agent checks VirusTotal for the second suspicious DLL, cdnver.dll, confirming its malicious classification and providing a verdict that this is a true positive.
Case Opened: Accelerating Resolution through Autonomous Action
Once confirmed, the agent automatically creates a case, maps the activity to MITRE ATT&CK, and sends email notifications to stakeholders. SOC analysts receive a fully pre-investigated case rather than raw logs, allowing them to focus on remediation rather than investigation.
Behind the Scenes: Building the Agent
The agent’s autonomy and reasoning tasks stem from its initial setup in the Elastic Agent Builder. By predefining the tools it can use, the goals it must pursue, and the schedule it follows, the agent can operate independently while the SOC team focuses on strategic oversight.
This model works because it transforms the SOC from a reactive posture to a proactive one. Elastic’s Attack Discovery correlates alerts generated by detection rules into a coherent attack chain, ensuring that stealthy activity does not remain buried in low-priority noise. The agents then confirm true positives automatically and close the loop with immediate case creation and notifications, drastically reducing dwell time. Most importantly, every step is auditable and transparent, providing the traceable context SOC analysts need to maintain full confidence in AI-driven operations and intervene only when human judgment is required.
External References
[1] https://machinelearningmastery.com/7-agentic-ai-trends-to-watch-in-2026/
[2] https://www.marketsandmarkets.com/Market-Reports/ai-agents-market-15761548.html
Frequently Asked Questions
What is an Agentic AI SOC?
It is an autonomous Security Operations Center where AI agents independently manage triage, investigation,response and other operational tasks. It shifts the focus from managing "alerts" to neutralizing "attacks" with minimal manual intervention.
Why should enterprises upgrade to an agentic model?
Industry is at a practical inflection point where governance and agent frameworks have matured for enterprise production, offering a strategic window to scale defense against a rapidly evolving threat landscape.
How does an Agentic AI SOC differ from a traditional SOC or AI copilot?
Autonomy. While a Copilot acts as a "passenger" that provides answers on command, an Agent is a "driver" that independently plans, executes, and coordinates complex investigations.
Do I need to know how to code to build and manage these agents?
No. Elastic Agent Builder uses natural language to translate strategic intent into autonomous behavior, allowing practitioners to "program" threat hunting agents without writing code.
Can an agent actually take response actions, like isolating a host?
Yes. Through integration with Elastic Workflows, agents can execute "guarded" actions, such as host isolation or case creation, once they meet your pre-defined confidence thresholds, while giving SOC analysts the option to review or intervene before critical actions are taken.
Is every action taken by an autonomous agent auditable?
Absolutely. Every decision is documented in a reasoning trace, providing a transparent audit trail that shows the exact logic, tools, and evidence the agent used.
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