Key Takeaways
- SOAR only covers 30–40% of alert volume — the rest go uninvestigated
- 40% of security alerts are never looked at, and 90% of investigated ones are false positives
- AI SOC doesn’t need pre-built playbooks — it reasons through alerts in real time
- AI SOC and SOAR aren’t enemies; they work better together than apart
- Secure.com’s SOC Teammate runs cases end to end, not just the first touch
Your SOC team received 960 alerts yesterday. SOAR automated response for maybe 350 of them through pre-built playbooks. The other 610 required manual triage – and many likely went uninvestigated due to analyst capacity constraints.
That’s not a staffing problem. That’s a fundamentally broken model — and it’s exactly the gap that AI SOC was built to close.
What SOAR Was Built to Do (And Where It Hits a Wall)
SOAR arrived with a promise: automate the repetitive parts of security operations so analysts could focus on what matters. For a while, it delivered. Pre-built playbooks sped up response on known attack patterns. Alert volumes dropped for the scenarios someone had already planned for.
The problem is that last part — someone had already planned for.
SOAR is a rule-based system. It executes what it’s been told to do. The moment a threat steps outside a defined playbook, SOAR stops. And in 2025, most threats step outside defined playbooks constantly.
Here’s what that looks like in practice:
- Playbook coverage caps out at 30–40% of alert volume, no matter how many engineering hours go in.
- Maintaining integrations becomes a full-time job — every vendor API update breaks something
- Building and tuning playbooks pulls skilled analysts away from actual threat investigation
- Most SOAR deployments take 12–18 months before showing measurable ROI
The SANS 2024 SOC Survey found that automation complexity had become the top barrier to effective SOC operations — ranked higher than staffing shortages. That says a lot about where SOAR has landed.
How AI SOC Is Fundamentally Different from SOAR
This is where the distinction matters most, and it’s not just a feature comparison.
SOAR executes workflows. AI SOC reasons through them.
When a SOAR platform gets an alert, it checks whether a matching playbook exists. If yes, it runs the playbook. If no, the alert sits until an analyst gets to it. There’s no judgment call happening — just pattern matching against what someone already scripted.
An AI SOC works differently. It ingests the alert, pulls context from across your stack (SIEM, EDR, IAM, threat intel), evaluates the situation using AI-driven analysis, and recommends investigation paths — without requiring pre-written playbooks. High-impact actions still require human approval to maintain governance. Think of it as the difference between a checklist and a trained analyst.
How AI SOC changes playbook ownership
In a SOAR environment, playbooks are built, maintained, and fixed by engineers. They break when APIs change. They go stale when your environment shifts. Someone has to own them — and that ownership cost compounds over time.
AI SOC flips this. Instead of human-authored playbooks that the machine runs, the system generates investigation paths dynamically, at runtime, based on the specific alert context. The “playbook” becomes an output of the investigation, not a prerequisite for it.
What AI SOC adds on top of SOAR workflows
Rather than replacing SOAR, AI SOC extends what it can cover:
- Alert triage at scale — AI investigates every alert, including the ones no playbook was built for
- Adaptive case management — cases are tracked, enriched, and escalated with full context attached
- Autonomous investigation depth — not just classification, but root cause, blast radius, and recommended containment
- Continuous learning — the system gets better as it sees more of your environment
A Cloud Security Alliance (CSA) study found that analysts assisted by AI completed alert investigations 45–61% faster and were 22–29% more accurate than their manual counterparts. Secure.com’s SOC Teammate shows similar improvements: early deployments demonstrate 70% faster detection (MTTD) and 50% faster response (MTTR). That’s not a marginal improvement — it changes how a SOC operates shift to shift.
When Should Security Teams Choose AI SOC Instead of SOAR?
SOAR earns its place on high-volume, predictable scenarios where the conditions are stable and confidence is high. Phishing email quarantine, known malware hash blocking, user account lockout on credential stuffing — these are exactly what SOAR was built for.
But the moment an alert requires judgment — correlating signals across tools, assessing whether unusual behavior is actually malicious, determining blast radius — SOAR reaches its ceiling.
Which SOAR limitations make the strongest case for AI SOC
A few signals that your SOAR investment has hit its limit:
- Alert backlog keeps growing despite playbook additions
- Engineers spend more time fixing broken integrations than building new ones
- Analysts are triaging manually what should be automated
- New attack patterns arrive before playbooks can be written for them
According to SACR’s 2025 AI SOC Market Landscape research (based on a survey of 300+ CISOs), 40% of alerts are never investigated. Of those that are investigated, 90% turn out to be false positives. This creates a dual problem: critical threats may hide in the uninvestigated 40%, while analyst time is consumed by the 90% false positive rate in the investigated portion. That’s the real cost of a coverage ceiling.
AI SOC doesn’t replace SOAR. It covers the other 60–70% of your alert volume that SOAR was never going to reach.
How Secure.com’s SOC Teammate Closes the Gap
Most AI SOC tools summarize alerts or assist with triage. Secure.com’s SOC Teammate runs the case end to end – from detection through investigation to response and closure.
That distinction matters. A summary tells an analyst what happened. End-to-end case management means the system ingests signals from SIEM, EDR, IAM, cloud, and email security — normalizes and enriches them with threat intelligence – executes pre-approved playbooks for containment (with human approval for high-impact actions) and tracks the case with full context and audit trail until resolution.
Here’s what that means operationally:
- Alert triage coverage increases from the industry baseline of ~40% to 95% through automated analysis, with full rationale attached to every decision via AI Trace
- Every action is explainable through AI Trace — a full audit trail with timestamps and reasoning, not a black box
- Human-in-loop controls let SOC leaders set approval thresholds — routine tasks run automatically, high-risk actions require human sign-off
- Deploys in 30 minutes across 200+ integrations, including CrowdStrike, Splunk, IBM QRadar, Palo Alto Networks, AWS, Azure, and GCP
Early deployments show 70% faster detection (MTTD) and 50% faster response (MTTR). Teams report saving 2,000+ analyst hours annually, with 176 hours saved per month on average through automated case handling and reduced manual triage.
Where SOAR requires ongoing engineering effort to maintain playbooks and integrations, the SOC Teammate works within your existing stack from day one — no playbook library required to get started, no 12-month implementation timeline. You start seeing value in 30 minutes after connecting your main systems (cloud, IdP, ticketing, SIEM).
If SOAR gave your team automation for what you anticipated, Secure.com’s SOC Teammate handles what you didn’t.
FAQs
Is an AI SOC better than SOAR?
Does AI SOC replace SOAR, or does it work alongside it?
Should we invest in AI SOC or SOAR first?
How does AI SOC improve outcomes from alerts generated by SOAR?
Wrapping Up
SOAR was the right answer for its time. The problem is that the threat landscape moved and SOAR’s architecture didn’t.
Sixty to seventy percent of your alert volume is outside what playbooks typically cover. That’s not a gap you can hire your way out of — with an average of 247 days to hire a security analyst and 12,486 unfilled cybersecurity positions, the talent market won’t support it. It’s a gap that requires a different kind of system: one that reasons, adapts, and investigates without needing a human to script every step.
Secure.com’s SOC Teammate is built for exactly that. Not another tool to manage – a Digital Security Teammate that works the queue, explains every move through AI Trace, and escalates only what genuinely needs a human decision while keeping humans in the loop for high-impact actions.
