AI vs. AI: What 2026 Will Mean for the SOC
In the last few years, we’ve watched generative artificial intelligence move from assistive technology to a promising force multiplier in security operations for forward-thinking organizations. In 2025, we crossed a threshold in the cybersecurity world: attackers began orchestrating campaigns with AI systems coordinating reconnaissance, execution, and adaptation at machine speed. While automation in cyber attacks isn’t new, the use of AI agents raises the stakes. More adaptable, cheaper, and faster.
As we move into 2026, the conversation shifts from whether AI belongs in the SOC to how it will fundamentally transform security operations. Three key trends will define this transformation:
- Agent-led investigation goes mainstream – Autonomous systems will handle investigative heavy lifting, with forward-leaning teams taking first steps toward AI-driven auto-remediation
- Domain-specific agents emerge as distinct from generic automation – True agentic systems built for security operations will separate from rebranded workflows as algorithmic breakthroughs unlock new capabilities
- The market hits peak “AI agent” fatigue – Security teams will demand proof over promises, forcing overdue distinction between genuine agents and automation with better marketing
From Human-Led Triage to Agent-Led Alert Investigation
One of the most important changes underway is where investigations begin.
When attackers rely on AI to iterate quickly, rewriting phishing content, testing controls, or adjusting tactics in near real time, manual investigation and static playbooks become increasingly untenable. Defense must adapt to keep pace. 2026 will mark the year we truly see agents versus agents—automated offensive systems met by equally sophisticated defensive counterparts.
This shift is already visible in how forward-thinking security teams are deploying defensive agents as their first line of investigation. Instead of attackers having the luxury of machine-speed reconnaissance while defenders crawl through alerts manually, we’re moving toward a more balanced playing field where defensive agents can match the pace of offensive automation.
Agentic investigation solutions fundamentally change that starting point. Instead of handing a human an alert and asking them to figure it out, autonomous agents can continuously gather evidence, correlate signals across tools, test hypotheses, and assemble a coherent narrative of what actually happened—all built from security experts who understand how real investigations unfold. These systems operate at the same speed as their adversarial counterparts, analyzing attack patterns and identifying indicators of compromise faster than any human-led process could manage.
By the time an analyst engages, they’re not staring at fragments. They’re reviewing context, timelines, and conclusions that are already grounded in evidence and shaped by seasoned security practitioners. The investigative heavy lifting—the correlation of logs, the timeline reconstruction, the hypothesis testing—has already been completed by systems that don’t suffer from alert fatigue or shift changes.
The Next Frontier: Agents Taking Action
The most forward-leaning security teams will begin taking their first cautious steps toward AI-driven auto-remediation in 2026. While investigative agents are still emerging and comfort levels remain mixed across the industry, I expect we’ll see these organizations experimenting with systems that don’t just diagnose threats, but also take immediate containment actions. This represents a fundamental shift from purely reactive security operations to proactive, machine-speed defense. These early adopters will look to identify scenarios where AI agents have proven most reliable and the risk of operational disruption is minimal.
The Rise of Truly Domain-Specific Security Agents
Another clear trend is the maturation of domain-specific AI agents built for security operations.
Many teams today struggle to bridge the gap between promising AI proof-of-concepts and reliable operational systems. Their implementations often work well in demos but fail under real-world conditions, never reaching the production quality needed for security operations.
This quality gap reflects where we are in AI agent development. Teams with specialized expertise in agent-based training methodologies (including our team at Embed) are finding success in building reliable, domain-specific systems. But the broader market is still waiting for these capabilities to become more accessible.
This challenge connects to one of the most exciting areas of AI research happening right now: discovering which algorithms work best when combined with large language models to produce optimal agent behavior. The 2016 breakthrough with AlphaGo offers a compelling parallel. AlphaGo’s success came from pairing deep neural networks with the right algorithm, namely, Monte Carlo tree search, which had emerged from research published a decade earlier. That combination proved transformational for the game of Go.
We’re still in the early phases of researchers identifying the algorithms that will unlock the full potential of LLMs for autonomous agents. As these breakthrough combinations are discovered and become more widely available, they’ll benefit everyone building domain-specific AI agents, not just the teams with deep expertise in agent-based training today.
In cybersecurity, this translates to agents that move beyond generic reasoning to develop true domain expertise. Instead of relying on general-purpose models layered with automation, these systems will learn to investigate the way experienced analysts do—asking the right questions, gathering the right evidence, and reasoning through complex scenarios with procedural accuracy.
The Market Reaches Peak “AI Agent” Fatigue
At the same time, the market is hitting a wall. Over the past year, nearly every automation feature has been rebranded as an “AI agent.” In many cases, nothing material changed. Static workflows, rigid playbooks, and scripted responses were simply wrapped in new language.
The breaking point is here. Security teams who’ve been burned by overhyped solutions are now asking pointed questions that separate genuine agentic systems from marketing spin:
- “Show me how it investigates something it’s never seen before.”
- “Walk me through its reasoning on this alert.”
- “What happens when our environment changes?”
- “Can it explain why it reached this conclusion?”
This pressure is forcing a much-needed distinction between genuine agentic systems (those capable of autonomous reasoning) and tools that are essentially automation with better branding.
That tension should eventually help reduce noise in the market and reward systems that are built on real AI foundations, not shortcuts.
What This Means for the Modern SOC
Taken together, these trends point toward a SOC that looks very different than it did even a few years ago.
Investigations will increasingly begin with autonomous agents that never get tired, never skip steps, investigate 100% of alerts, and know how to correlate data. Analysts can spend less time chasing false positives and more time applying expertise where it truly matters. The resulting trust, earned through transparency and consistent reasoning, becomes the deciding factor in whether AI is adopted or ignored.
Our perspective at Embed is shaped by deep experience in both AI and cybersecurity. We’ve lived the reality of alert fatigue and repetitive triage. We’ve seen how quickly confidence erodes when tools don’t explain themselves. That’s why we believe agentic security has to be built with accountability at its core, autonomy with visibility, and speed paired with clarity.
The SOC of 2026 isn’t about replacing people with AI. It’s about finally giving security teams systems that can keep up with modern threats and show their work along the way.
