The End of “More”: Re-Architecting the Modern SOC with Agentic AI
For the last decade, the cybersecurity industry has attempted to solve the “SOC problem” with a singular, flawed strategy: More.
We built more dashboards to visualize data. We deployed more tools to collect telemetry. We hired more Tier 1 analysts to stare at screens. Yet, despite this exponential growth in investment, the fundamental metrics of security operations—Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR)—have plateaued across the industry.
The cognitive load on analysts has reached a breaking point. The “eyes on glass” model is failing, not because analysts aren’t working hard, but because the volume of noise has outpaced human processing speed.
Over the past year at Proficio, we realized that adding incremental improvements to a strained model was no longer sufficient.
We didn’t need a better dashboard; we needed to fundamentally re-architect the investigation path.
We are proud to introduce the Proficio AI Operator.
This isn’t just another chatbot summarizing tickets or a “Copilot” that waits for instructions. It is an architectural shift where AI acts as an active operator, supporting our analysts across every stage of an alert’s lifecycle—from log ingestion to triage, correlation, investigation, and escalation.
The goal isn’t to replace the analyst. The goal is to ruthlessly eliminate the waste between the signal and the decision. Here is a deep dive into the five pillars of this transformation and why it represents the future of Managed Detection and Response (MDR).
1. From Passive Summarizer to Agentic Workflows
Transforming the Modern SOC with Agentic AI – Driven Solutions
The first wave of Generative AI in security was largely passive. You could feed an LLM a log line, and it would explain what the log meant. Helpful? Yes. Transformational? No.
To truly scale a SOC, AI needs to move from Generative to Agentic.
An “Agentic” workflow means the AI possesses the autonomy to execute a multi-step plan. When an alert triggers in the Proficio environment, the AI Operator doesn’t just wait for a human to ask a question. It proactively begins the investigation.
It acts like a seasoned investigator:
- It Asks Questions: Instead of just flagging a login anomaly, it asks, “Is this user traveling? What is their typical baseline? Have we seen this IP address across other clients?”
- It Pivots: If it finds a suspicious hash, it doesn’t stop there. It pivots to check if that hash was executed on other endpoints.
- It Suggests Next Steps: It formulates a remediation plan based on the evidence found.
By automating the “OODA Loop” (Observe, Orient, Decide, Act) at machine speed, the AI Operator reduces the investigation time significantly. It hands the analyst a near-complete case file, not just a raw alert.
2. RAG Built Specifically for Security
Retrieval-Augmented Generation (RAG) is the mechanism by which AI retrieves external data to answer a query. However, general-purpose RAG (like you might find in standard public LLMs) is dangerous in a SOC environment because it lacks domain specificity. It might pull outdated advice from a 2018 forum post.
We have built a Security-First RAG architecture. We aren’t just retrieving general internet knowledge; we are applying targeted retrieval across four distinct pillars of validated evidence:
- Client History: The system recalls previous incidents specific to your environment. It knows your “normal.”
- Threat Intelligence: It pulls real-time data on active campaigns and IOCs from Proficio’s global threat feeds.
- OSINT (Open Source Intelligence): It automates the validation of external IP reputations and domain registries.
- Prior Escalations: It “remembers” how senior Proficio analysts handled similar alerts in the past, learning from human decisions.
This curation is critical. By narrowing the aperture to only validated evidence, we ensure that the AI is grounded in the reality of your specific network architecture, rather than generalities.
3. Multi-Alert Reasoning: Connecting the Dots
One of the greatest failures of traditional SIEM deployments is the “atomic alert” problem.
- Alert A fires for a firewall deny.
- Alert B fires for a failed login.
- Alert C fires for a PowerShell execution.
Viewed in isolation, these might be dismissed as low-priority noise or “informational” tickets. However, when viewed as a sequence, they represent a clear kill chain.
Proficio’s AI Operator utilizes Multi-Alert Reasoning. It does not look at signals in a vacuum. It correlates signals across the entire stack—Endpoint (EDR), Network (NDR), Cloud, and Identity.
The AI identifies the narrative connecting these disparate points. It understands that the firewall deny and the PowerShell execution involve the same user identity, even if they occurred on different devices. This ability to elevate the right incidents, rather than just the loudest ones, is the key to defeating alert fatigue. It allows our analysts to focus on complex narrative attacks rather than whacking moles.
4. Tight Hallucination Controls: The Trust Barrier
The elephant in the room regarding AI in cybersecurity is hallucination. In a creative writing context, an AI making things up is a quirk. In a SOC, an AI inventing a CVE or fabricating an IOC is a catastrophe that wastes time and erodes trust.
We have implemented rigorous, deterministic guardrails to solve this. The AI Operator is not given free rein to “be creative.”
- Bounded Outputs: Every step the AI takes is bound to specific ServiceNow fields and verified data schemas.
- Verification Layers: The system cannot cite a CVE unless it exists in the vulnerability database. It cannot recommend blocking an IP unless that IP is present in the telemetry.
- Fact-Checking: We utilize a “critic” model approach where a secondary layer verifies the reasoning of the primary operator before it is presented to the human.
There are no fabricated vulnerabilities, no invented IOCs, and no imagined actions. If the data isn’t there, the AI states that the data is missing—it does not invent it to fill the void. This determinism is what makes the system enterprise-ready.
5. Analyst-in-the-Loop: The Exoskeleton Approach
Perhaps the most important aspect of our re-architecture is the role of the human.
There is a narrative in the industry that AI will replace the Level 1 Analyst. We disagree. We believe AI elevates the Level 1 Analyst to perform at the level of a Level 3 Hunter.
We utilize an Analyst-in-the-Loop design. The AI Operator handles the tedious, repeatable, and high-volume data crunching. It presents a hypothesis, the gathered evidence, and a recommended course of action.
The analyst stays in control. They review the reasoning. They correct assumptions. They validate outcomes.
This creates a continuous feedback loop. Every time a Proficio analyst corrects the AI, the system learns. Every time the analyst validates a finding, the confidence score for that pattern increases. This is not automation for automation’s sake; it is a force multiplier that drastically reduces cognitive load. It allows our security talent to do what humans do best:
apply intuition, understand business context, and make high-stakes judgment calls.
Why This Matters: The Future of MDR
The cybersecurity landscape is facing a convergence of pressures that traditional models cannot survive:
- The Talent Gap: It is widening, not closing. We cannot hire our way out of this problem.
- Alert Volume: As digital transformation accelerates, log volume explodes.
- Attack Velocity: Attackers are using AI to speed up their exploitation. Defenders must use AI to speed up their response.
SOCs that successfully combine human judgment with AI-driven investigation will be the only ones capable of scaling efficiently and safely.
We’re already seeing measurable movement in our own SOC. We are seeing faster investigations, more consistent escalations, and a tangible improvement in the focus of our analysts.
This is the future of MDR. Not a black box that makes decisions for you, but a transparent, intelligent operator that elevates the entire security posture.
If you’re interested in where this goes next—or how the AI Operator integrates with your specific stack—let’s talk. Contact Us.