Arambh Labs: AI Agents Designed for Always-Changing Cyber Threats
Neha Garg, Co-founder and CEO
It’s the question every CISO faces as alert volume climbs and each new tool adds more noise than clarity. Over the past decade, the industry tried to automate its way out of overload. SOAR platforms promised relief but turned into systems that needed constant rewrites. Every new alert type required another playbook, exposing the limits of rule-driven automation that scripted systems can’t keep pace with attackers who refuse to follow scripts.
“Out of these tens of thousands of alerts each month, only about one percent require human judgment. Everything else becomes routine work that should be handled by machines but can’t be, because the systems available today follow rules instead of thinking through problems,” says Neha Garg, co-founder and CEO of Arambh Labs. “Security teams need a platform that can investigate every alert end to end and reason through unfamiliar scenarios without manual intervention.”
That is exactly what Arambh Labs built.
Its Agentic AI platform approaches investigations the way a seasoned analyst would, forming plans in real time based on the alert, the environment and the tools already in place. A swarm of specialized agents works across identity, cloud, endpoint and network systems, delivering full-stack visibility, contextual prioritization and machine-speed response in minutes and surfacing only the alerts that deserve human attention.
How the Swarm Works
The strength becomes clear in how the agents collaborate. Rook interprets the incoming alert and maps the investigation. Byte executes across the organization’s SIEM, EDR, identity platforms, cloud infrastructure and network tools. As evidence surfaces, Echo expands the search with proactive threat hunts. Talon cross-references indicators against known threats and dark-web infrastructure.
When they encounter an alert type the system has never seen, they reason through how to investigate it rather than throwing an error or escalating prematurely. That reasoning happens because of SecLM, Arambh Labs’ security-trained language model built to behave with the discipline analysts expect. The model produces valid syntax for any tool in the stack, interprets evidence with rigor and applies consistent logic whether the alert is familiar or unprecedented.
Built for Real Environments
The platform fits how organizations actually operate. Many face strict data-residency and compliance requirements, so Arambh Labs offers both on-premises and SaaS deployment. For MSSPs running 100- plus customer environments, each with different tools and regulatory frameworks, that flexibility becomes essential. MSSPs can also offer the platform under their own brand through white-label and reseller partnerships.
One MSSP demonstrates the impact. The provider managed multiple enterprise clients but couldn’t scale threat hunting without bottlenecks. Analysts struggled to write effective queries for Google SecOps SOAR, leaving the SOC perpetually reactive. After deploying Arambh Labs, investigations began moving automatically. The platform now generates correct queries for each client’s SIEM and runs full end-to-end investigations without human intervention. Mean Time to Remediate dropped up to 90 percent. Echo conducts continuous hunts across every environment, catching threats analysts would never have had time to pursue.
The strength becomes clear in how the agents collaborate. Rook interprets the incoming alert and maps the investigation. Byte executes across the organization’s SIEM, EDR, identity platforms, cloud infrastructure and network tools. As evidence surfaces, Echo expands the search with proactive threat hunts. Talon cross-references indicators against known threats and dark-web infrastructure.
When they encounter an alert type the system has never seen, they reason through how to investigate it rather than throwing an error or escalating prematurely. That reasoning happens because of SecLM, Arambh Labs’ security-trained language model built to behave with the discipline analysts expect. The model produces valid syntax for any tool in the stack, interprets evidence with rigor and applies consistent logic whether the alert is familiar or unprecedented.
Built for Real Environments
The platform fits how organizations actually operate. Many face strict data-residency and compliance requirements, so Arambh Labs offers both on-premises and SaaS deployment. For MSSPs running 100- plus customer environments, each with different tools and regulatory frameworks, that flexibility becomes essential. MSSPs can also offer the platform under their own brand through white-label and reseller partnerships.
One MSSP demonstrates the impact. The provider managed multiple enterprise clients but couldn’t scale threat hunting without bottlenecks. Analysts struggled to write effective queries for Google SecOps SOAR, leaving the SOC perpetually reactive. After deploying Arambh Labs, investigations began moving automatically. The platform now generates correct queries for each client’s SIEM and runs full end-to-end investigations without human intervention. Mean Time to Remediate dropped up to 90 percent. Echo conducts continuous hunts across every environment, catching threats analysts would never have had time to pursue.
What Comes Next
The team is developing capabilities that use offensive insights to improve detection itself, refining how threats get caught before they escalate. The goal is a system that learns continuously and connects the fragmented pieces of security operations into something coherent and forward-looking.
That vision came from founders who understood where automation failed. Pooja Singh spent 15 years watching traditional SOAR tools break the moment environments changed. Garg brought experience building production AI that remained reliable at scale. Shivani Sharma added depth in machine learning. Amit Kumar contributed expertise in large-scale systems.
The team is developing capabilities that use offensive insights to improve detection itself, refining how threats get caught before they escalate. The goal is a system that learns continuously and connects the fragmented pieces of security operations into something coherent and forward-looking.
That vision came from founders who understood where automation failed. Pooja Singh spent 15 years watching traditional SOAR tools break the moment environments changed. Garg brought experience building production AI that remained reliable at scale. Shivani Sharma added depth in machine learning. Amit Kumar contributed expertise in large-scale systems.
Trusted by Fortune 500 companies and MSSPs, Arambh Labs empowers organizations to outpace adversaries and reclaim control of their security posture. What the platform delivers isn’t incremental improvement over playbook automation. It’s infrastructure that operates the way effective analysts think, handles what it hasn’t seen before and works at the speed and scale modern threat environments demand.Out of these tens of thousands of alerts each month, only about one percent require human judgment. Everything else becomes routine work that should be handled by machines but can’t be, because the systems available today follow rules instead of thinking through problems. Security teams need a platform that can investigate every alert end to end and reason through unfamiliar scenarios without manual intervention.
