Agentifying the SOC: How Agentic AI Can Power Autonomous CTI Operations

If there’s one thing to know about cybersecurity, it’s this: just when you think you’ve reached the pinnacle of innovation and adapted to a game-changing breakthrough, the ground shifts beneath you and rewires everything you thought you knew about the domain.

The rapid evolution of cybersecurity has outpaced static automation and siloed workflows. Not long ago, the legacy Security Orchestration, Automation, and Response (SOAR) platforms were thought to transform security operations by automating manual tasks with high-code playbooks. But over time, the limitations of these legacy SOAR platforms, from rigid structures to fragile integrations and limited adaptability, started to show. These limitations were overcome to a good extent with low-code/no-code automation platforms. And now, with the advent of AI agents, the pace of change has only accelerated over the past year, demanding a complete rethinking of how we defend against threats. I’ve seen this shift firsthand, from manual incident response to legacy SOAR, to low-code/no-code automation, and now to something far more advanced.

Today, we're witnessing the emergence of truly autonomous security operations powered by collaborative AI agents that think, adapt, and act with near-human intelligence.

The SOAR Legacy: Brilliant, But Bound by Design

SOAR platforms were a breakthrough in cybersecurity operations. By turning analyst expertise into repeatable playbooks, they brought much-needed consistency and speed to incident response. For many teams, SOAR helped tame the chaos of manual workflows and laid the foundation for more structured, measurable security operations.

As the threat landscape evolved, however, new challenges began to emerge. Traditional SOAR platforms, built on rigid, predefined logic, excelled in responding to known threats, but struggled to adapt to novel tactics or shifting attacker behaviors. Playbooks required constant manual updates. Integrations were often brittle and inflexible. And many platforms remained siloed, limiting their ability to respond to threats in context or collaborate across broader ecosystems. Low-code/no-code automation helped resolve many of these issues to a good extent.

Looking ahead, the demands on security operations are clear: automation must be adaptive, intelligence-led, and deeply integrated. Static, non-agentic SOAR architectures are increasingly mismatched for today's pace and complexity. The future calls for systems that can learn, reason, and respond dynamically, turning automation into a true force multiplier rather than a fixed process pipeline. 

Cyware is actively building toward this future-forward vision, developing and enhancing agentic capabilities that will transform SOAR into intelligent, adaptive security orchestration.

The Age of AI Agents

The transformation toward AI agents is already underway. A single autonomous agent deployed to monitor threat feeds. Another configured to enrich indicators of compromise. A third tasked with correlating security events across multiple data sources.

What began as isolated experiments in efficiency is evolving into something much larger: the early stages of a distributed intelligence network, one that will operate continuously across the security infrastructure of tomorrow.

Some of these agents are already embedded within today’s security stack- in threat intelligence platforms, endpoint detection, and parts of vulnerability management. Others are just emerging, poised to take on more complex, contextual decision-making roles in the near future. This shift signals more than just incremental progress. We are moving toward artificial intelligence that approaches human-level situational awareness in cybersecurity. These agents will increasingly understand not just what is happening, but why it’s happening, what it might lead to, and how to act in alignment with larger defensive strategies.

The implications are profound. Security operations that once demanded full-time analyst teams may soon be augmented by intelligent agents that never rest, continuously improve, and expand the reach of human expertise. 

This isn’t about replacing analysts; it’s about building a future where they are empowered by always-on, learning-capable digital counterparts.

Multi-Agent Orchestration: The Collective Intelligence Paradigm

To understand the true potential of agentic security operations, we must move beyond thinking about individual AI agents and focus on how they collaborate within multi-agent systems. These coordinated networks of specialized agents represent the next evolutionary leap in cybersecurity architecture. 

Consider a sophisticated, intelligence-driven threat hunting scenario unfolding in real time: advanced threats are proactively identified through behavioral analysis and anomaly detection, revealing subtle Indicators of Compromise (IOCs) that traditional tools often miss.

Operationalized Threat Intelligence for Enhanced Security Operations

An Intelligence Gathering Agent continuously monitors millions of threat feeds simultaneously, applying dynamic relevance criteria that evolve with emerging threat patterns. The moment a significant indicator surfaces, perhaps a new malware signature associated with a known threat actor group, this agent immediately signals an Enrichment Agent.

The Enrichment Agent doesn't simply cross-reference the indicator against known databases. Instead, it performs sophisticated analysis in coordination with a Correlation/Hunting Agent, examining the indicator's relationship to historical attack campaigns, analyzing its potential connection to current geopolitical events, and assessing its relevance to the organization's specific threat model. Simultaneously, it enriches the indicator with contextual metadata including confidence levels, source reliability assessments, and temporal relevance scores.

While this enrichment occurs, a Risk Assessment Agent evaluates potential organizational impact by analyzing the indicator against the company's current asset inventory, known vulnerabilities, and existing security controls. This agent considers not just direct technical impact but also business continuity implications, regulatory compliance requirements, and potential cascading effects across interconnected systems.

As these parallel analyses develop, a Remediation Planning Agent synthesizes the collective intelligence and automatically initiates appropriate response workflows to the Threat Actioning Agent. This might involve deploying additional monitoring agents to watch for related indicators, triggering preventive controls on critical systems, or escalating the threat to human analysts when the confidence level or potential impact exceeds predetermined thresholds.

Simultaneously, a Sharing and Collaboration Agent facilitates information exchange with trusted partners and threat intelligence communities. Rather than sending generic alerts, these agents craft targeted communications that provide each recipient with information relevant to their role and responsibilities. Technical teams receive detailed indicator information and recommended actions, while executive stakeholders receive high-level risk assessments and business impact analyses.

This entire sequence, from initial indicator detection to coordinated response, occurs in seconds, not hours or days. 

The multi-agent system enables security operations centers to detect, investigate, and respond to threats with speed and accuracy that surpasses traditional human-driven processes while maintaining the contextual understanding that purely automated systems lack.

The Communication Challenge: Building Trust in Distributed Intelligence

As multi-agent systems become more prevalent and sophisticated, a critical challenge emerges: ensuring secure, efficient, and contextually rich communication between agents. Traditional API-driven integrations are insufficient for this level of collaboration. These agents need to share not just data but the reasoning behind their conclusions, the confidence levels of their assessments, and the contextual factors that influenced their decisions.

This requirement for rich communication extends beyond technical data exchange. Agents must convey intent, negotiate task allocation, resolve conflicts, and adapt to changing operational priorities. They need to establish trust relationships, verify each other's integrity, and maintain security even when operating across distributed environments and multiple organizational boundaries.

The protocols that enable this communication form the backbone of agentic security operations. Without robust inter-agent communication standards, we risk creating fragmented islands of capability that cannot achieve the collective intelligence necessary for modern cyber defense.

Evaluating Communication Paradigms: The Protocol Landscape

Several approaches to agent communication have emerged, each with distinct characteristics that shape how agents interact and collaborate. Understanding these paradigms is crucial for organizations seeking to implement agentic security operations.

Google's Agent-to-Agent (A2A) Protocol

Google's Agent-to-Agent (A2A) Protocol, Built on HTTP, JSON, SSE, and RPC, represents a significant advancement in enabling cross-platform agent communication. Built on open standards, A2A promotes interoperability across heterogeneous environments, allowing diverse AI systems to exchange messages seamlessly regardless of their underlying platforms or vendors. This flexibility proves invaluable in today's complex digital ecosystems where organizations must integrate numerous tools and services.

A2A excels in large-scale deployments spanning multiple domains and geographic locations. Its open design encourages innovation and reduces barriers to integration, making it attractive for enterprises prioritizing broad connectivity. However, the protocol's inherent generality means it lacks the specialized features required for high-stakes cybersecurity environments. Cyber defense demands more than basic data exchange. It requires deep contextual awareness, real-time trust verification, and adaptive coordination capabilities that current A2A implementations don't natively support.

Anthropic's Model Context Protocol 

Anthropic's Model Context Protocol The Model Context Protocol (MCP), developed by Anthropic, is an open standard that connects AI models to real-world systems content repositories, business tools, and development environments through a standardized, JSON-RPC-based interface. Think of it as the USB-C of AI: a universal connector that eliminates the need for custom integrations, enabling AI systems to fetch relevant data and perform actions across various platforms with ease.

MCP simplifies AI integration with interoperability, real-time responsiveness, and built-in security. It enables developers to build reusable agentic servers accessible by any MCP-compatible AI client. Persistent connections allow assistants to receive live updates and interact dynamically with external tools and data stores via standardized access. This transforms siloed language models into context-aware business agents, supported by robust NLP for natural, conversational interactions.

Despite its promise, MCP is still maturing and presents real challenges. Security remains a top concern, particularly around prompt injection and access token misuse. Operational complexity can also pose hurdles, especially when configuring local MCP servers. As adoption grows, organizations must weigh the benefits of flexibility and standardization against the risks of early-stage implementation, data exposure, and LLM limitations.

IBM's Agent Communication Protocol

IBM's Agent Communication Protocol emphasizes enterprise needs, focusing on security, compliance, and reliable message delivery. Designed for complex IT environments requiring auditable, trustworthy communications among AI agents managing sensitive data and processes, this protocol prioritizes predictable workflows and robust security postures.

This approach proves particularly valuable for organizations where regulatory compliance and data governance are paramount. Its emphasis on security and reliability makes it dependable for mission-critical applications that cannot tolerate communication failures or security breaches.

However, the protocol's focus on fixed workflows and strict structures can inhibit the adaptive, flexible collaboration required in modern threat environments. Without native support for rich contextual data exchange or dynamic role reassignments, agents become constrained by rigid processes that limit their ability to respond agilely to novel or rapidly evolving attack scenarios.

CISOs, Here's What Matters Now: Native Context Protocols and the Rise of Agentic SOAR

Agentic SOAR platforms represent a paradigm shift in cybersecurity operations. Unlike generalist protocols, these systems incorporate purpose-built inter-agent communication mechanisms designed specifically for threat intelligence and security workflows.

These native protocols enable agents to share rich contextual metadata- threat confidence, urgency, constraints, and rationale and use intent-aware messaging to coordinate complex tasks, negotiate responsibilities, and escalate issues based on evolving threat landscapes. Continuous trust verification ensures only authorized agents execute sensitive actions, a critical safeguard in distributed or externally integrated environments. This transforms traditional SOCs into intelligent, autonomous ecosystems capable of rapid, accurate detection, investigation, and response.

CISOs must prioritize Agentic SOAR platforms with natively integrated model context protocols, not generic context or communication  frameworks. This cyber domain-specific architecture doesn't just improve operations; it transforms security response capabilities entirely.

In a world where seconds define outcomes, this investment is not just worthwhile- it’s essential.

Natural Language Playbook Development with Agentic SOAR: Democratizing Security Automation

The shift from traditional SOAR to agentic orchestration redefines how security playbooks are designed and executed. Legacy systems depended on rigid, pre-coded workflows that were difficult to update and ineffective against emerging threats. In contrast, agentic SOAR uses multi-agent systems and natural language processing to dynamically create and adapt response protocols in real time.

Now, security teams can build complex workflows simply by describing desired outcomes in plain language. 

For example: “When lateral movement is detected in financial systems, isolate affected endpoints, preserve forensic evidence, and alert compliance teams based on severity and data classification.” The system interprets the input, coordinates appropriate agent actions, and executes the response with built-in oversight and escalation logic.

This approach empowers analysts to translate expertise into action without coding. They can adjust workflows mid-execution using natural language, adding investigation steps, refining responses, or modifying escalation paths, enabling seamless collaboration between human judgment and machine precision.

Operational Benefits of Agentic SOAR

An agentic SOAR framework simplifies operations by enabling AI agents to interact seamlessly with tools across the security stack from threat feeds and vulnerability databases to SIEMs and ticketing systems. This unified orchestration layer eliminates the complexity of managing fragmented integrations.

Organizations can model, run, and optimize long-running threat intelligence processes within a single environment, cutting down on administrative overhead and manual coordination across disparate platforms.

The approach also supports significant vendor consolidation, lowering total cost of ownership while bridging gaps between threat intel analysts and automation teams. Standardized agent interactions replace ad hoc toolchains, reducing licensing costs, maintenance burdens, and integration complexity.

Building Tomorrow's Security Operations

As threat actors grow more sophisticated, static workflows and traditional tools can’t keep up. Agent-based orchestration introduces contextual intelligence, dynamic adaptability, and coordinated automation, enabling faster, more precise responses. This marks a shift from simple automation to true collaboration, where machines reason alongside humans to enhance decision-making.

General-purpose AI protocols like A2A or MCP hint at this future but fall short in the high-context demands of cybersecurity. In contrast, purpose-built platforms with native context protocols are becoming the foundation of intelligent SOCs. 

They support dynamic role assignment, real-time trust checks, and intent-aware messaging, transforming brittle threat intelligence workflows into autonomous, adaptive operations that are continuously evolving and ready.

We are no longer talking about future possibilities; we are operationalizing them today. If you're looking to understand how agentic AI can transform your security operations from reactive to truly autonomous, now is the time. Get in touch to explore how agentic orchestration can future-proof your CTI strategy.