Arambh Labs Named Among 11 Startups to Watch in Agentic AI by Gartner
Earlier this year, Gartner named Arambh Labs one of only 11 startups to watch in agentic AI in its March 2026 report, Emerging Tech: AI Vendor Race — Startups to Watch in Agentic AI.
Gartner reviewed a field of 129 funded startups building in agentic AI. Eleven made the list. We are honored to be one of them, and we have been sitting with what the recognition means as the category has accelerated around us.
The report speaks for itself. What we can add is what we believe the recognition reflects, why agentic AI has become the most consequential shift in security operations in a decade, and what we are building at Arambh Labs.
Why agentic AI, and why now
For most of the last decade, "AI in security" meant models that scored, ranked, or flagged things. Useful, but fundamentally passive. A model that tells you an alert is 87% likely to be malicious still leaves a human to do everything that follows: pull the logs, check the identity, correlate across tools, decide, document, and act.
Agentic AI is a different idea. Agents do not just assess; they work. Given an alert, an agent builds an investigation plan, gathers evidence across your stack, reasons through what it finds, reaches a verdict, and drives the response, adapting as the situation unfolds rather than following a script written in advance.
That difference matters most in exactly one place: work that is high-volume, high-stakes, and impossible to fully script. Security operations is the textbook case. Alert volume outgrew human triage capacity years ago. Attackers automated and now move in minutes. And no playbook library ever written covers the attack nobody has seen yet. The SOC is where agentic AI stops being a technology trend and becomes an operational necessity.
That is the wave Gartner's report title points at, an AI vendor race, and it is very real. Analysts, buyers, and builders have all converged on the same conclusion at the same time: the next generation of security operations will be run by reasoning agents, supervised by people, and the platforms being built right now will define what that looks like.
What we think this recognition reflects
We can only speak for ourselves, but we know what we have been relentless about since day one, and we believe it shows.
We build for production, not demos. From our first deployment, the bar has been real enterprise traffic in real environments. The platform has now investigated more than 1,000,000 alerts in production across 25+ deployments, from cloud-native scale-ups to regulated enterprises. Demos are rehearsed. Production is not. We chose the harder proof.
We build agents that reason, not scripts that execute. The founding conviction of Arambh Labs is that rule-based systems fail in a reasoning-required world. Our agents plan each investigation for the specific alert and environment in front of them, which is why they handle the novel scenarios that break playbook automation.
We build for both attack surfaces. Organizations are not just defending with AI agents anymore. They are deploying them: coding assistants, support bots, automations with credentials and API access. That makes agents attack surface, and almost nothing watches them. Arambh Labs was built as agentic detection and response for agents and non-agents alike: one platform that runs your SOC with AI agents and secures the AI agents you run.
We build for trust. Autonomy without auditability is a liability. Every verdict our agents deliver ships with its complete reasoning trail: what was checked, what was found, how the conclusion follows. Your analysts can audit everything. So can your auditors.
"When we started Arambh Labs, agentic AI was a research direction, not a market category," said Neha Garg, cofounder and CEO of Arambh Labs. "We bet that reasoning agents would transform security operations, then did the unglamorous work of making that bet hold up in production. This recognition tells us the category has arrived. Our job has not changed: earn trust one investigation at a time, with the evidence attached."
What this means if you run a SOC
Recognition is encouraging, but it is not the point. The point is what the platform does for the teams that run on it, and that story has been consistent across every deployment.
Alert coverage goes to 100%. Every alert gets a genuine end-to-end investigation the moment it fires, which no human team can do at enterprise volume. Response compresses from hours to minutes, because investigation no longer waits for a person to reach the alert. And the human workload changes shape: only the small fraction of alerts that genuinely need judgment, roughly 10% in our production deployments, ever reaches an analyst, already investigated, with the evidence attached. The rest closes autonomously.
Your team keeps control of what matters. Consequential actions wait for human approval. The agents operate inside the policies, boundaries, and escalation paths you define. And because everything is auditable, trust is something you can verify rather than something you are asked to extend.
Just as importantly, the platform watches the newest part of your attack surface. The AI agents your own teams deploy hold credentials and take real actions, and most security tooling was never built to see them. Ours was.
On the category, honestly
We will say something vendors do not usually say in recognition posts: the agentic AI race Gartner is tracking is crowded, well-funded, and full of genuinely capable companies. That is good. Categories only become real when serious builders compete in them, and security teams only win when vendors are pushed to prove their claims.
Our advice to buyers evaluating this space has not changed, and it applies to us as much as anyone: do not buy the demo. Run platforms against two weeks of your own alerts. Compare verdicts against your own analysts' judgment. Open ten closed alerts and read the reasoning. Test an alert type the platform has never seen. And ask every vendor what it can tell you about the AI agents already operating in your environment. We wrote a full evaluation guide on exactly this, because we are confident about where those tests lead.
Momentum, and what we do with it
The recognition came at a moment of real momentum for Arambh Labs: production deployments spanning cloud-native scale-ups to regulated enterprises, a platform that now covers both sides of the agentic shift, and a team that has been building toward this category since before it had a name, with leadership that architected security automation at Fortinet and built frontier LLM products at Google.
What we do with it is more of what got us here. Deeper reasoning. Broader coverage across the stack our customers already run. Continued investment in runtime security for AI agents, because that attack surface compounds every quarter. And the same operating principle throughout: proof over promises, evidence attached.
To our customers, thank you for trusting us with the work that matters most and for pushing us to be better every week. To our team, this recognition belongs to you. And to the security leaders watching the agentic AI race and wondering what is real: we would love to show you, on your own alerts.
Book a demo to see the platform on your own queue, or read our guide to choosing an AI SOC platform if you are earlier in the evaluation.
Gartner, Emerging Tech: AI Vendor Race — Startups to Watch in Agentic AI, March 2026. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner's research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
