From SIEM to AI SOC: The Agent-Driven Future
How AI agents will transform security operations from alert-driven chaos to intelligent, autonomous analysis that finally scales to fit our needs.
Welcome to Detection at Scale, a weekly newsletter for scaling security operations teams, focused on best practices applying AI agents in the SOC. If you enjoy reading Detection at Scale and find it helpful, please share it with your network!
The SOC has a fundamental scaling problem. Not only are there too many alerts to monitor, but performing the job effectively requires high technical nuance across understanding operating systems, networks, cloud environments, attacker tactics, and the latest intelligence. Working in the SOC is also stressful, error-prone, and requires high attention to detail. Querying an incorrect timeframe, missing one event, or failing to check a related log source could wildly change the course of an investigation or introduce the organization to significant levels of risk. Precision and speed matter in security operations.
Throughout the years, security teams have tried various solutions to these problems. We adopted detection as code, built deterministic response automation to contain incidents and deeply understand alerts, and adopted data lakes to handle new scale needs.
AI agents introduce a new category of automation that can finally address the fundamental scaling constraints facing security operations. This capability shift demands changes in SIEM architecture, team expectations, and the infrastructure foundation enabling effective AI-powered security operations.
Pattern Matching
During the last two decades in cybersecurity, we witnessed several fundamental shifts—from the emergence of modern SIEMs in the early 2000s, through the introduction of EDRs in the mid-2010s and XDRs in the late 2010s, to the adoption of data lake architectures and SOAR platforms, and now the integration of AI in a new context. Yet one constant remains: humans must ultimately always determine “good” versus “bad” in alerting and security operations. Why is this? Because attempting to automate every possible attack permutation would result in inaccurate, impossibly complex, and unmaintainable security code.
Security operations teams are very sophisticated pattern matchers. When alerts come in, like when our infrastructure team adds an IAM role that can be assumed from an AWS account outside of our organization, we typically know exactly why (because we just spoke with them about it) or we have a gut feeling that “oh, this is bad.” There are too many possible conditions to predict ahead of time to avoid that alert, and hence, we are “flooded with alerts,” which has been the canonical problem to solve in cyber. But what else is a very sophisticated pattern matcher? A large language model (LLM).
Generative AI has high potential to automate most routine security operations tasks because LLMs can process vast amounts of context, instructions, tools, and data, then produce a complete analysis. When we prompt a model, every additional word (token) guides its attention to the right place. This means we can break from rigid, traditional automation and begin delegating novel tasks to AI agents, which are LLMs with carefully crafted prompts that specify personas and goals. As long as the model is given the appropriate depth and variety of context, it can perform nearly as well as a human analyst. But if it’s missing any key business or security contexts, it will naturally perform worse and be perceived as a hallucinated inferior solution.
Understanding that LLMs excel at sophisticated pattern matching opens the door to how we structure security operations workflows, moving from single-point-of-failure bottlenecks to AI agents that can operate across the entire security lifecycle.
The Multi-Agent SOC
The security team’s free time is a very fleeting resource. Between on-call rotations, incident response fire drills, and the constant pressure to stay current with new types of threats, analysts are burning out at alarming rates. The real challenge is the cognitive load of making high-stakes decisions under pressure, often with incomplete information. When you factor in the need for continuous learning, it becomes clear that throwing more people at the problem isn’t sustainable.
We need to fundamentally change how security operations work gets distributed between humans and machines, allowing analysts to focus on the strategic, creative problem-solving that humans excel at while delegating the repetitive, context-heavy tasks to AI agents.
There are several opportunities to apply agents across the lifecycle of security operations:
Threat Hunting and Modeling: What’s important for our organization to protect? Do we have the data to back that up? Can we find the indicators of an attack?
Detection Creation: What behaviors do we need to track? Which ones deserve an on-call page? What are our security significant events?
Incident Response: What do we do once we get paged? How do we assess/react/recover?
Let’s start with threat modeling agents, which provide a massive speed improvement in querying and understanding our vast security data that we spend large sums of time and money collecting. These agents can analyze your data using natural language and perform research on particular tactics/techniques, search for evidence of indicators, or look around to discover high-priority assets, baseline behavior, and map potential attack paths. Traditional threat modeling exercises happen quarterly at best and quickly become stale, but an AI agent can help maintain a living threat model that updates as your infrastructure changes, new vulnerabilities are disclosed, and landscapes shift.
For Detection Creation, AI agents can bridge the knowledge gap between our security team’s monitoring needs and how these get implemented as actionable rule logic. Rather than spending months or years learning specialized syntax to translate business logic into detection rules (a process that often takes weeks), agents can quickly assess your available data and unique environment characteristics and generate tailored detections that just work. This process quickly becomes a flywheel, where the more high-quality rules created and optimized, the easier net-new creation becomes. Additionally, incident response and triage agents can feed learnings into detection creation agents.
Finally, Incident Response, which often takes the most time and creates the most stress. For most security teams that haven’t yet applied AI in this area, they tend to create a playbook for a given type of alert, document a series of steps and if/else logic for handling the alert, then apply the playbook to one or many rules. The problem is that every incident has a unique context that doesn’t always fit neatly into predetermined logic trees.
AI agents can fundamentally change this by acting as triage assistants that intelligently combine the technical details of an alert with the broader business context and the identified leads during triage. Imagine an agent that automatically correlates a suspicious login attempt with recent employee departures and historical attack patterns against your industry. Instead of 30 minutes of manual research, the agent delivers a rich briefing in only 2-3 minutes: “This login attempt from Romania targeting Sarah’s account is concerning because she left the company last week, her access should have been disabled, and we’ve seen similar patterns in recent attacks against financial services companies.” The agent doesn’t always make the final decision, but it arms the human analyst with the context needed to make an informed judgment quickly.
The Platform Shift
Traditional SIEM platforms weren’t designed for the kind of rich, contextual analysis that AI agents require. Legacy systems store data in proprietary formats, limit access through rigid query interfaces, and charge prohibitive costs for the data volumes that effective AI agents need to consume. The shift toward data lake architectures creates the foundation that AI agents need to be truly effective. When security data lives in open formats in data lakes, agents can access vast amounts of historical data without the performance bottlenecks or cost penalties of traditional systems, enabling them to analyze years of data to understand normal patterns, seasonal variations, and subtle attack progressions that would be impossible with limited data retention.
The “connective tissue” enabling AI SOC evolution extends far beyond data architecture, and SIEM platforms will likely evolve to fulfill this critical role. This requires robust APIs for agent interactions with security tools, comprehensive data catalog management for handling diverse log formats, and sophisticated identity and access controls that enable agents to operate securely throughout your environment. Security data pipelines have become essential—not merely for cost optimization, but for ensuring AI agents can access clean, enriched, and properly formatted data.
Most importantly, the infrastructure needs to support “context engineering,” the practice of systematically providing AI agents with the business context, threat intelligence, and environmental knowledge they need to make informed analysis. This means maintaining knowledge bases about your assets, business processes, risk appetite, and operational procedures in formats AI agents can consume and reason about.
Evolution Determines AI Success
While humans have remained the final arbiters of security alerting for decades, AI agents can now shoulder the exhausting work of context gathering, pattern analysis, and routine decision-making that overwhelms security teams. The organizations successfully deploying AI agents are embracing architectures purposefully designed for this new paradigm. Data lakes, open formats, flexible APIs, and a robust data fabric are requirements for survival in the modern security landscape.
For security teams ready to embrace this evolution, the potential for step-function improvement is tangible and immediate. The fundamental scaling problem that has plagued SOCs—too many alerts, too much context to gather, too few analysts with too little time—finally has a viable solution to build upon. The question isn’t whether this transformation will happen, but whether your current SIEM platform can power it or will become an obstacle to progress.
AI agents can transform every aspect of the security operations lifecycle. The agent-driven SOC is being deployed today by forward-thinking teams that understand the power of combining human expertise with AI capabilities. The journey from traditional SIEM to AI-powered security operations starts with choosing infrastructure that can truly support this vision—and the time to begin is now.
Thanks for reading! I’m the Founder and CTO at Panther, building intelligent AI agents and security data infrastructure to automate and accelerate core security operations workflows. If you want to learn more about how Panther incorporates security pipelines, open data lakes, signals/detection layer, and AI agents into its platform, check out our demo or book a meeting with me below! Panther is trusted by leading security teams like Coinbase, Asana, Discord, and more.
