Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo When we started building Torq four years ago, we had a thesis: the SOC was broken, and automation — real automation, not another tool bolted onto the stack — was the way to fix it. AI has since changed the game entirely. But has it streamlined the SOC, or introduced new complexity? We wanted to find out.

Chris Jacob, Field CISO, Securonix For years, security operations has been measured by effort. More alerts are reviewed. More logs are ingested. More tools are deployed. More dashboards are built. On paper, that can look like progress. In practice, many CIOs know better.

Every major shift in security operations starts with a shift in the underlying platform. The AI era is no different. As artificial intelligence moves from novelty to necessity, the real dividing line in cybersecurity will not be which vendor can add AI features the fastest. It will be which platforms are built on the right foundation to make AI useful in real operations and trustworthy when the stakes are high. That foundation is data, but not in the simplistic sense the market often uses the term.

See how Torq harnesses AI in your SOC to detect, prioritize, and respond to threats faster. Request a Demo Security teams are being asked to move faster and handle more complexity, while the threats they defend against are increasingly AI-assisted. When I wrote about VoidLink in January, my point was simple: you cannot fight machine-speed threats with human-speed defense. Attackers are using AI to code, adapt, and scale attacks while humans are still grinding away doing the heavy lifting in the SOC.

When SOAR emerged around 2015, it was trying to solve a real problem: SOC analysts were drowning in manual, repetitive tasks across disconnected tools. SOAR promised to connect those tools, automate the workflows between them, and give analysts their time back. For a while, it mostly delivered. That era is long dead.

There’s a growing acceptance that AI is no longer optional in security. That battle is largely won. The more interesting question — and the one I keep getting asked — is what we actually believe AI should be responsible for, and where human authority must ultimately sit.

Security teams are being asked to do more than ever, often with fewer people and less time. As alert volumes continue to rise and adversaries automate their attacks, even mature SOCs struggle to keep pace. Legacy tools surface signals, but they still leave analysts responsible for triage, investigation, and response decisions that take time and experience to execute well. CrowdStrike Charlotte AI was built to change that model.

According to the SACR 2025 AI SOC Market Landscape report, 40% of security alerts go uninvestigated. The average alert investigation takes 70 minutes. Meanwhile, attackers achieve breakout in under 48 hours. That math doesn’t work in anyone’s favor — except the adversary’s.

Tool proliferation is compounding. Alerts are multiplying faster than teams can triage them. Visibility gaps are hiding real threats. And security teams are stuck babysitting archaic security infrastructure, rather than detecting and stopping threats. Organizations across gaming, fintech, and retail are feeling the weight of traditional, on-premises SIEMs.

If you’ve been in security operations for more than a few years, you’ve lived through the automation hype cycle at least twice. First, it was SIEM that was going to solve everything. Then SOAR was supposed to fix what SIEM couldn’t. Now, AI SOC platforms are delivering what SOAR always promised but never actually could.

In its January 2026 report, Emerging Tech: Tech Innovators in Domain-Specific Language Models for SecOps, Gartner examines how domain-specific language models (DSLMs) are reshaping security operations. The report explains that DSLMs are designed to address the limitations of general-purpose language models by focusing on a particular task or use case – in this case, cybersecurity.