Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Networking teams have invested heavily in automation to help them manage increasing workloads and reduce manual tasks. Yet many still face the same issues, like outages, stalled operations, and managing growing incident volume. This problem isn’t a lack of automation: it’s what happens after automation runs. Automation is useful for individual tasks, but it can’t handle the complexity of real-world networking processes, which demand coordination across teams, environments, and tools.

Runbooks are supposed to be the safety net under operations. Unfortunately, most aren't because they live in wikis that decay as tools change, get linked from alerts but never consulted, and fail the responder the moment pressure arrives. The gap is between what the runbook says and what the responder can actually execute. Teams reach for AI to close the gap.

The Tines Voice of Security 2026 report found that security professionals spend 44% of their time on manual, repetitive work. A workflow engine is the software built to take that operational drag off people, deciding what happens next based on events, rules, and state. The category is shifting. The workflow engine used to live inside one system, running a narrow set of backend steps.

When a security alert fires, your analyst opens your security information and event management (SIEM) platform, copies an IP address, pastes it into a threat intelligence platform, checks the asset inventory, cross-references the identity provider, and messages the on-call lead on Slack. Meaning your analyst needs to wade through five tools, taking at least ten minutes before any actual response begins.

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo 92% of security leaders say something is actively reducing their trust in AI within the SOC. These aren’t skeptics, they’re people who have already adopted AI and believe in its ability to enhance security operations. We know from the 2026 AI SOC Leadership Report that AI is already widely adopted in the SOC, with 94% of organizations using it in some capacity.

Most teams adopting AI in their workflows understand that LLMs do not behave like traditional software. The same input does not always produce the same output, and even when it does, the model can be wrong, manipulated, or misled. Hallucinations happen even without adversarial input. Air Canada learned this in 2024 when a tribunal ordered the airline to honor a bereavement-fare refund policy its support chatbot had invented out of thin air.

Many security functions today still rely heavily on humans for detection, triage, and response, often by design. But as environments grow more complex and alert volumes explode, it raises a hard question: Can this approach scale on its own? Adopting AI in security operations isn’t just about adding tools. It means rethinking the SOC operating model itself — roles, workflows, and team structures. Here’s why, and how.

Every security vendor shipping an AI product in 2026 makes the same promises. Faster triage. Shorter response times. Fewer false positives. Reclaimed analyst hours. But, six months after deployment, most security leaders still cannot answer a straightforward question from the board: Is this thing actually working?

I’m an old engineer at heart. Many of my ideals were formed by Joel’s Things You Should Never Do, Fred’s No Silver Bullet, and Brian’s Big Ball of Mud. One of my favorites was Greenspun’s Tenth Rule: The joke isn’t really about programming languages. It’s about a pattern: certain problems have a shape, and no matter how you approach them, you end up building the same solution, in the same order, until you arrive at the same messy place.

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo Rick Bosworth is a cybersecurity marketing executive with nearly two decades of experience driving GTM strategy across technology startups. His uniquely technical perspective bridges the gap between complex solutions and practical customer outcomes. Rick has deep expertise spanning EDR, CNAPP, CWPP, AppSec, CTEM, and agentic SecOps.