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Data layer security for the Agentic era.

See every access. Know the intent. Stop risky use. At runtime.

TRUSTED BY LEADING ENTERPRISES WORLDWIDE

With the rapid growth of AI, knowing which identities, especially non-human ones, are accessing your data and how is more important than ever. Aurva is redefining access monitoring by tying every action to a real identity across agents, humans, and services.

Chris Bream
Former Head of Security Engineering

Aurva gives us real time, identity aware visibility into data access, helping us prevent unauthorized use and privilege escalation while meeting regulatory guidelines. As access becomes more agentic and ephemeral, we rely on Aurva to tie queries to identities and flag anomalies. It is a critical layer in keeping production safe at scale.

Ashwath K.
Head of Security

The Blindspot

You granted access. Not permission to misuse it.

One prompt can trigger tool calls, API requests, database queries, and data sharing. By the time you look, the evidence is fragmented across systems.

Agent Identity & Intent

Data Accessed & Sensitivity

Destination & Exposure

The Deeper Problem

Authorization tells you it was allowed. Not whether it was appropriate

The real problem is not just who was allowed in. It is whether the actual data activity was appropriate in runtime.

Turn runtime truth into right-sized access

See actual data use.
Fix excess access before it becomes a risk.

Capture the full chain behind every data interaction: identity, action, data touched, destination, and outcome.

Runtime Attribution

Know who acted — agent, human or NHI

Trace every query, change, or deletion to the real identity. Know the full chain behind agentic action.

DFM →

Agentic Chain →

Appropriate Data Use

Authorized ≠ Appropriate

See what sensitive data was touched. Know when legitimate activity becomes inappropriate at runtime.

DFM →

AIRuntime →

Right-Size Permissions

Reduce excess access with runtime

Identify over-permissioned identities, detect privilege drift, and right-size permissions,

AccessIQ →

What the truth layer caught

Same Prompt. Same Agent. Different Risk.

Authorization would allow both. Runtime context would not.

User PromptPull compensation data for annual review

Access Chain

user→comp-copilot→MCP→payroll_db

Query Executed

SELECT ssn FROM hr_employeesPII:SSN

User PromptPull compensation data for annual review

Access Chain

user→comp-copilot→MCP→payroll_db

Query Executed

SELECT ssn FROM hr_employeesPII:SSN

Without runtime context, both would look allowed

RUN A

In Scope

PurposeConsistent

Access matches what this agent was built to do

Scale of AccessProportionate

42 rows accessed - Only what tasks required

Where data wentExpected

Internal HR data warehouse:Workday HCMSnowflake

Context verified

RUN B

Purpose Drift

PurposeInconsistent

SSN & Address - Never cleared for this agent

Scale of AccessDisproportionate

1.24M rows accessed - Volume has no legitimate explanation

Where data wentUnexpected

First time agent routed to external end-pointapi.openai.com

Access exceeded declared purpose

How it works?

From Telemetry to Truth to Action

Criticality based on real risk. Context from runtime. Proof for every finding. And a fix ready to apply.

20Bn+

Access Events monitored daily

0ms

Application latency impact

<2%

Alert False Positive rate

35+

Enterprise Protected

Chosen by teams who need Evidence, not Guesses

The Aurva team went above and beyond, even integrating with some of our custom environments to deliver the right outcomes. Aurva gave us zero-impact data flow visibility, helped us map our sensitive data end-to-end, and reduced overexposure risks. Thus, working as a true partner to our security and engineering teams

Siddharth Gupta

Senior Architect, Meesho

As one of the only cloud-native banks, Aurva was the only DAM tool that gave us real-time, granular visibility into database access — critical for meeting banking Infosec norms.

Sovon Lal Mukherjee

CISO, Slice Bank

We started with Aurva for egress monitoring, but their results and commitment made it an easy decision to expand to DSPM, Data Flow, and DAM. They’ve become a critical part of our data security stack

Ashwath Kumar

Head of Security, Razorpay

Chris Bream

Former Head of Security Engineering, Meta

We connected Aurva directly into our SIEM, and it’s helped us surface issues and database accesses we would have otherwise missed — especially during developer-driven database migrations or schema changes. Rather than reacting after something goes wrong, we now have proactive visibility and control.

Sandeep Srinivasan

CISO, Fampay

We deployed Aurva and within an hour got to know the different regions where we are sending data. Context Enrichment of Egress Monitoring also helps in understanding whether these third parties take any sensitive data or transactional data. I realised the power of the tool when I literally saw 2 day efforts being reduced to minutes

Tushar Anand

Lead Security Engineer, Jupiter

Aurva provides deep visibility into egress traffic, database activity, and the classification of sensitive data across Razorpay’s infrastructure. It strengthened our security posture while ensuring compliance requirements were met. Aurva’s simple deployment, scalability, and reliability - seamlessly matching Razorpay’s high-traffic demands.

Mani

Lead Security Engineer, Razorpay

Aurva Platform helps us monitor our cloud traffic and identify any malicious activity. Fills a void in our security stack, helps us monitor traffic continuously from a security point of view and identify any malicious traffic. We are able to monitor traffic from VMs, Kubernetes clusters seamlessly.

Suresh Khema

Head of Platform Engineering & Infrastructure, Apna

I want my security engineering team to focus on key business metrics enabling secure development of these applications. We chose Aurva because we saw an opportunity to comply with Regulatory Compliances and Digital Personal Data Protection Act, 2023