DAM for the AI Era

Database activity monitoring (DAM) is a security control that continuously records and analyzes every query made against a database, who ran it, what they accessed, and whether it deviates from normal behavior, so security teams can detect misuse, enforce least privilege, and meet compliance requirements like PCI-DSS, SOX, and HIPAA. Traditional DAM tools rely on native database logs or network taps; modern, AI-era DAM tools like Aurva also resolve queries back to the actual human, service account, or AI agent responsible, not just a shared credential.

Database activity monitoring (DAM) watches activity: the live stream of queries hitting a database in real time. Data security posture management (DSPM) assesses posture: where sensitive data lives, how it's classified, and who has standing access to it, typically as a point-in-time or periodic scan. The two are complementary: DSPM tells you where your risk is; DAM tells you when that risk is being exploited. Aurva runs both from a single runtime platform so posture findings and live activity share the same identity and data-sensitivity context.

Most legacy DAM tools cannot. They were built to log queries by service account or connection string, so an AI agent using a shared database credential looks identical to any other automated process: the tool sees the account, not the actor. Aurva was built specifically to close this gap: it resolves every query to the real actor (human, service account, or AI agent), builds a behavioral baseline per agent, and flags deviations, for example when an AI agent suddenly queries a table outside its normal scope.

It depends on the architecture. Agent-based DAM tools typically add 5-10% CPU overhead on the monitored host; native-logging approaches can add 20-40% database CPU overhead at high query volume; proxy-based tools add 5-10ms of latency per query because traffic is rerouted through a middle layer. Aurva uses a passive, out-of-band architecture that reads traffic without sitting in its path, which is how it monitors 20Bn+ queries per day across customer environments with 0ms added application latency.

Legacy DAM tools (Oracle Audit Vault, native database auditing, most agent-based products) were designed for long-lived, on-prem database servers and often lose visibility when databases are ephemeral, serverless, or auto-scaled: the agent or log pipeline can't keep up with instances that spin up and down. Aurva was built agentless-first for cloud-native environments, with agent, agentless, and hybrid deployment modes, and covers 25+ data stores including PostgreSQL, MySQL, MongoDB, Snowflake, BigQuery, DynamoDB, and Kafka.

Authorization is a static grant: a role or permission that says an identity can query a table. Appropriateness is contextual: whether that specific query, at that specific time, from that specific actor, matches how the data is supposed to be used. A finance analyst with legitimate read access to a customer database is authorized to query it; that same analyst exporting the entire table at 2am from an unfamiliar location is authorized but not appropriate. Legacy DAM and IAM tools only check the first condition. Aurva checks both, in real time.

DAM pricing varies widely by vendor and is typically based on number of monitored database instances, data volume, or number of protected data stores, and is rarely published publicly by enterprise vendors (IBM Guardium, Imperva, Oracle) since it's negotiated per deployment. Contact Aurva directly for pricing based on your environment. Get a demo to see a scoped quote.