Matters.AI, an India-built data security company, has announced that its AI Security Engineer platform is securing more than 100 petabytes of distributed cloud data at Groww, an Indian investment platform. The company says the deployment has reduced manual compliance and audit efforts by more than 80%, with the platform detecting, assessing, and resolving over 90% of qualifying critical security risks without analyst intervention.
The deployment addresses a challenge facing enterprises as their cloud infrastructure expands: security teams must track growing volumes of data across multiple environments while managing alerts, identifying exposure, and preparing for audits.
Automated security across 100 petabytes of data
According to Matters.AI, Groww’s expanding data infrastructure had created visibility gaps across business units and cloud environments. Security teams faced increasing alert volumes, limited capacity for manual investigation, and recurring compliance tasks.
Matters.AI deployed its platform within Groww’s environment through a phased onboarding process. The company says the system brought more than 100 petabytes of data under unified security controls without a measurable impact on production performance.
The deployment also identified previously unknown shadow data assets across dozens of core infrastructure projects. These assets can remain outside established security oversight, creating potential exposure if they are not identified and managed.
Matters.AI reports a mean time to detect unauthorised data exposure of less than one minute.
More than 90% of critical risks resolved automatically
The platform uses automated workflows to move certain high-confidence security alerts from detection to remediation without sending every case to a human analyst.
Matters.AI says more than 90% of the critical risks covered by this process are detected, assessed, and resolved end-to-end, typically within four minutes.
The company clarified that the figure applies to a defined category of high-confidence critical risks. It does not represent the automation of all security operations. Human involvement remains necessary for cases that require investigation, judgment, or decisions beyond the platform’s automated workflows.
The approach is intended to reduce the operational burden on security teams, allowing analysts to focus on issues that require closer attention.
Compliance workload falls by more than 80%
The deployment has also changed how Groww handles compliance and audit preparation. Matters.AI says automated data classification and continuous generation of audit-ready evidence have replaced much of the manual work involved in recurring compliance exercises.
As a result, manual compliance and audit effort has fallen by more than 80%, according to the company.
The reported reduction could be significant for organisations managing large cloud environments, where collecting evidence and maintaining visibility across distributed systems can consume considerable staff time. However, the announcement does not provide a breakdown of the measurement period or the methodology used to calculate the reduction.
Groww and Matters.AI on the deployment
Prajal Kulkarni, chief information security officer at Groww, said the platform had helped address visibility gaps and integrate compliance-related telemetry into operational workflows.
“By neutralising our visibility blind spots and embedding compliance-grade telemetry directly into our operational workflow, Matters.AI has allowed us to scale our security architecture at the speed of business innovation,” Kulkarni said.
Keshava Murthy, CEO and co-founder of Matters.AI, said the deployment demonstrated how an AI-based security system could operate at petabyte scale while allowing a lean security team to manage a growing infrastructure.
What the deployment signals for cloud security
Groww’s implementation highlights a shift from security systems that primarily monitor environments and generate alerts to platforms that can also take action against selected risks.
The reported results point to potential gains in detection speed, remediation, and audit preparation. At the same time, the extent of automation depends on which risks the system can confidently identify and resolve. The figures disclosed by Matters.AI are company-reported, and the announcement does not include an independent assessment of the results.
For enterprises expanding their cloud infrastructure, the key question is whether automated security controls can maintain consistent performance as data volumes and operational demands increase, without creating new risks or removing necessary human oversight.