In an era where cyber threats are becoming faster, more sophisticated, and AI-driven, traditional Security Operations Centers (SOCs) are struggling to keep up. Rule-based systems, static playbooks, and manual analyst workflows are no longer enough to combat the pace and complexity of modern attacks.
To stay ahead of adversaries, Chief Information Security Officers (CISOs) and SOC leaders must rethink how security operations are designed—not just by upgrading tools, but by fundamentally transforming how threats are detected, investigated, and neutralized.
This is where Agentic SOC Platforms come into play.
Unlike conventional SOAR (Security Orchestration, Automation, and Response) systems that rely on static automation scripts and rigid playbooks, Agentic SOC platforms introduce a new paradigm: intelligent, autonomous systems powered by AI agents that continuously adapt, learn, and act. These systems combine Large Language Models (LLMs), Machine Learning, and context-aware reasoning to elevate the SOC’s capabilities from reactive to proactive.
Autonomous threat detection and triage using AI agents
Proactive incident investigation and response with minimal human intervention
Continuous security intelligence and contextual awareness
Dynamic workflows that evolve as threats and environments change
Scalable, efficient operations without relying on predefined scripts
Agentic SOCs, like those offered by Prophet Security, Hunters' Pathfinder, and Intezer Autonomous SOC, showcase the future of autonomous cybersecurity operations—where AI agents not only automate tasks but collaborate and reason through complex attack scenarios.
In this blog, we’ll dive deeper into the core capabilities of Agentic SOC platforms, explore how they're reshaping modern security teams, and examine real-world examples driving this shift in the cybersecurity landscape.
The above figure shows a simplified Agentic SOC (Security Operations Center) architecture that illustrates how layered pieces - from human analyst interfaces to autonomous agents and Reasoning powered by LLMs - all work together to detect, investigate, and responds to threats. The arrow flow emphasizes the sequence of data ingestion, orchestration, reasoning, and execution of action, all as one calibration loop. The model intends to optimize the process through automation while allowing analyst decision oversight.
The agentic SOC platform's core function's core function is advanced data exploration, and this enables explorers to make real-time decisions within complex security ecosystems. Traditional SOCs have struggled with things like data silos, legacy SIEMs (Security Information and Event Management) platforms, and overwhelming amounts of alerts. Agentic platforms tackle these problems by correlating security technologies (SIEM, EDR, threat intelligence feeds) and unify them to provide a 360-degree view of the threat landscape.
Tools like Ripjar Labyrinth for Threat Investigations (LTI) make use of Generative AI to sift through unstructured data at scale and speed—piling in external threat intelligence to provide meaningful risk. When an Agentic platform detects a suspicious login attempt, it can automatically correlate the IP addresses, bound them to geolocation data, look at user behavior and history, and if this is determined to be legit, leave it be. When an agentic platform does this, it will provide its analysts with adequately contextualized packets of data, using tools like Torq's Socrates—the analyst gets a human-readable report, not a pile of stray data.
The hallmark of an agentic platform is the ability to be multimodal (text, logs, network, etc.), enabling detection of possible user feedback waiting for attention. These platforms can find valuable patterns and anomalies using LLMs and hundreds of fine-tuned operational workflows with security polishing tasks, which inherently reduces the false positives and creates room for the SOC to focus on higher priority risks. Real-time visualization tools allow analysts to interactively understand the potential to pivot around multiple data points to give depth to the attack narrative.
Proactive detection and response are essential for stopping cyber threats before they escalate into an incident. Agentic SOC platforms are best equipped for this kind of reactive and proactive detection and response by utilizing anomaly detection, predictive analysis, and autonomous decision making. This is a significant advantage over "traditional rule-based detection systems" because agentic solutions can learn from real-world data and apply it against new threat vectors, or emerging threats, while rule-based systems would fail completely.
For example, CrowdStrikes, Falcon platform, powered by Charlotte AI, leverages agentic characteristics to uncover patterns based on abnormalities in a signal. For instance, it could correlate login attempts from new ISPs and detect an unusual number of logins attempts as a signal of malicious activity. Separately, the agent could correlate signals from email security, identity management, or EDR logs to begin containment activities autonomously. The Falcon agent can take several containment actions automatically (e.g. quarantining emails, resetting user credentials, blocking a malicious IP) with zero human input. This action leads to a risk reduction in MTTD (Mean Time to Detecta) and MTTR (Mean Time to Respond) and consequentially, the impact of breaches is lessened when they occur.
Ongoing learning is essential for proactive defense. Agentic platforms increase their ability to assess threat intelligence data sets at scale, identifying what is a true threat and what is noise. Penlink's CoAnalyst can take a pile of data and create events and records based on queries with natural language so SOCs can hypothesize about attacks before they occur and be proactive instead of reactive. Integrated with external feeds and internal systems, agentic platforms keep SOCs one step ahead of the adversary, even with AI-driven attacks with breakout times of under two minutes.
Investigations take time for an SOC to conduct operations, as it often requires sifting through logs, correlating events, and reconstructing attack narratives, and while agentic SOC platforms still require review and edits from analysts, they can help with investigations by autonomously performing deep investigations and delivering well-structured reports.
Prophet Security's capabilities reflect the criteria of an agentic SOC platform, automating alert triage and investigation with accuracy. Upon triggering of an alert, Prophet's AI agents sift through logs, perform memory forensics, analyze URLs, and gather endpoint data to examine the threat's scope and impact. The report contains very structured responses that include a verdict indicating maliciousness, root cause analysis, and prescriptive next steps for remediation. This focus allows SOC analysts to continue with tasks like threat-hunting and strategic rather than repetitive data collection.
Agentic platforms are also uniquely suited for complex multi-step investigations. For example, Intezer’s Autonomous SOC integrates with SIEMs and EDRs for recursive analysis that assesses compromised accounts or systems across the environment. Since these forms of agentic platforms maintain state across investigative steps, no unwelcome detail can be omitted, even in extensive enterprise networks. The use of natural language processing also enables systems to be interrogated conversationally for more meaningful analysis.
Automation powers today's SOC; however, most traditional SOAR platforms are limited by static playbooks that hinder adaptable collaboration. Agentic SOC platforms avoid these issues, allowing for orchestration of workflows that are dynamic and contextual allowing the practice to scale around the enterprise. They can automate the repetitive and mundane tasks—triaging alerts, phish responses, incident containment—so the security analysts can focus their efforts on higher-value tasks.
Most of this is demonstrated through Torq's Multi-Agent System, which allows for agents to specialize in different tasks, (e.g., data enrichment, code generation, case summarization). As an example with a phishing incident, the Torq platform can autonomously analyze the phishing email's content, check the IOC reputations and conduct environment-wide sweeps to determine which users may have been affected by the phishing attack. Automated containment steps such as blocking domains or terminating sessions—are initiated automatically so the analyst can complete other higher value tasks. As a result, containment time is dramatically reduced.
Human-in-the-loop workflows allow for accountability because analysts can review actions taken by an automated workflow before they are executed. We have seen platforms like Hunters' Pathfinder AI combine agentic automation with guided investigations through workflows, suggesting next steps and refining detections from applicable real-world context in real time. This combination strikes the right balance between getting the most out of automation and human input, taking precaution in not leaning into completely agentic automation in high-stakes situations.
In the diagram below, we see how an Agentic SOC self-manages phishing threats. After identifying a phishing email, the AI agents will analyze the content of that email and extract any indicators of compromise (IOCs). The agents will then determine which customers are affected by creating the appropriate quantities of tickets and take the first steps to contain that threat. A user analyst will then review and approve before the incident response has been completed and logged, allowing for quick resolution, yet with human oversight.
Fig. 2 Automated Workflow for Phishing Incident
Although Agentic SOC platforms have the potential to be significant disruptors in the way the security industry operates, there are some challenges that come along with them. The inherently probabilistic aspects of LLMs give choice and variability to the deployments, which raises questions around reliance on the outcomes those agents will produce when these systems are deployed to production level use. Red Canary has identified that fully autonomous agents typically reduce the accuracy of workflows that require near-perfect accuracy, and to mitigate that require a full structured transparency and explainability of the platform to pursue documentation around any eventual decision-making process for the analyst to entrench a level of confidence.
Integration is also a significant hurdle. Agentic platforms have to link to different tools that already exist (SIEMs, EDRs, CMDBs, etc.) to provide value, and poor integration means an agentic platform could leave the user with just partial data, which negates the point of the platform. Intezer believes that AI agents should be equipped with the best possible supporting tools they can leverage for evidence collection, such as memory forensics collection, and reverse engineering for example, which would provide the investigator with a more robust evidence collection.
Additionally, scalability and cost should be taken into account. Agent platforms can decrease manual workloads, but because they produce high computational requirements across the organization, it requires an infrastructure that can handle them (e.g., IT / Cloud platform such as Microsoft Azure / AWS). Organizations must do a cost / efficiency assessment, and we must recognize that this can be more difficult for smaller SOCs with smaller budgets.
The future of agentic SOC platforms is very promising, as developments in large language models (LLMs), multi-agent orchestration, and domain-specific intelligence will continually advance. Gartner forecasts that by 2026, AI will enable SOCs to work 40 percent more efficiently and that security analysts will return to their traditional oversight roles over AI and the professional training of AI. The following notable trends are emerging:
Fig. 3 Emerging Trends of Agentic SOC Platform
Multi-Agent Collaboration: Agent orchestration platforms, e.g., agent at Aisera allow agents to collaborate across departments e.g. across HR, IT, security etc. thus, moving more smoothly through complex workflows.
Domain-Specific Intelligence: Hyper-specialized versions of LLMs always create more precise answers than general purpose LLMs, increasing accuracy in industries like banking and healthcare.
Graph-Based Threat Mapping: Agentic platforms will move beyond list-based Security Information Event Managers (SIEMs) to build a multidimensional graph database to visualize and neutralize complex forms of attack.
Responsible AI: Transparency, human oversight, and bias mitigation will play a critical role in the ethical deployment of agentic systems.
Companies such as Drop zone AI and Arcanna.ai are very innovative in applying agentic technology to next-generation SIEMs and hyper automation platforms, to name a few. As these technologies become more ubiquitous, we can expect SOCs to make the shift from reactive or preemptive to proactive, predictive operations to stay one step ahead of adversaries.
Agentic SOC platforms fundamentally reshape how cybersecurity is executed, providing SOCs with an innovative alternative to traditional SOAR and SIEM systems. These platforms offer advanced data exploration, proactive detection and response capabilities, deep dive investigations, automated workflows, and other use cases enabling SOCs to triage and respond effectively to emerging and contemporary threats.
Solutions like Torq, Prophet Security, Hunter’s Pathfinder AI, and many more, highlight just how autonomous AI agent technology can facilitate and assist security operations while caring for far deeper utilization of the data they need to secure their environment against constant threat vectors. This technology reduces burnout for analysts while remediating and in security operations keeping threats under control.
With the case for adopting agentic SOC platforms sitting at the forefront of the latest burgeoning technologies, organizations are well past the point of an option; investing in agentic SOC platforms is now a requirement. Organizations wanting to maximize their investment in agentic SOC platforms need to ensure organizations maximize integration and transparency with associated decision intelligence processes and workflows. In summary, as hacking and other cyber technological threats become fisher and more challenging, the future of the means and methods as noted in agentic SOC platforms revolves around autonomy, agility, and intelligence.