Aurva

How Aurva Compares to Imperva

Closing the Gaps in Data Security with Real-Time, Kernel-Level Observability.

Aurva: Built for a New Era of Data Security

Aurva was born from a problem its founders faced firsthand, i.e, understanding who is accessing what data and why in complex, large-scale environments. Traditional database logs and proxy-based monitoring tools struggled to capture real user intent, creating dangerous blind spots and exposing sensitive data to potential breaches.

Driven by this frustration, Aurva set out to eliminate these gaps, providing complete, real-time data observability. It captures every interaction from entry to exit, using continuous, kernel-level monitoring without the latency or overhead of conventional methods. This approach empowers security teams to detect risky access patterns, uncover shadow data flows, and take precise, actionable steps to protect sensitive data, even in AI-driven environments.

Deep Data Visibility for Modern Environments

Data security isn’t just about tracking database user logins. Aurva goes beyond basic logs and static alerts, providing deep context, precise anomaly detection, and comprehensive coverage for modern, AI-driven data environments.

Aurva doesn’t rely on predefined inventories and static thresholds, but continuously monitors every interaction, uncovering shadow data assets and detecting complex threats without the overhead or blind spots of legacy tools.

Features

Context Enrichment

Provides deep visibility into both machine and human identities at a granular level, capturing actual user activities.

Activities limited to DB user association, often missing context on real users or applications interacting with the database.

AI Powered behavioral detection

Aurva’s advanced AI engine leverages eBPF generated contextual data for precise anomaly detection; reducing false positives and identifying insider threats beyond basic compliance checks.

Static, compliance-driven alerts with minimal context and basic anomaly detection.

Future-Ready Data Coverage (including Stored Procedures)

Comprehensive support for modern databases, including vector DBs and AI application-level access.
Comprehensive visibility on Stored procedures and queries executed via the same

Limited support for modern databases
Lack support for stored procedure tracking

Shadow Data Asset Detection

Real-time network monitoring to discover shadow data assets, eliminating blind spots without requiring pre-defined inventories.

Requires a pre-defined data asset inventory from the customer, adding complexity to the onboarding process.

Document Classification

Identifies host of documents such as legal, finance, etc. Allows to custom as well.

Not Supported

End-to-End Data Flow Intelligence

Aurva completes the data security journey for organizations by leveraging DSPM to identify sensitive data assets, monitor access patterns, and track data flows across complex environments. This approach not only ensures compliance but also provides a clear view of how data moves within and beyond the organization.

Features

Data Discovery and Classification

Supports shadow data discovery.
Uses context-aware NLP models for high-accuracy classification of sensitive data.
Includes OCR for identifying sensitive data in images and scanned documents.

Does not provide data discovery and classification features.

Enhanced Context through DSPM

Enables custom policies based on real-time context through Aurva’s DSPM, aligning data access with security requirements.

Requires manual work or integration with additional tools to understand the full context of data access activities and apply relevant policies.

Visibility into Data Flows

Offers complete visibility into data-in-motion, tracking how databases, applications and third-party services interact with each other.
Provides real-time insights into authorized and unauthorized access, enabling real-time anomaly detection.

Primarily logs database access activities but lacks full visibility into how data moves between applications or external services.
Requires correlating database logs with other sources to understand broader data movement.

Cloud DLP (Egress Monitoring)

Provides real-time egress monitoring, detecting third-party data sharing and potential exfiltration risks in cloud environments.

Primarily focused on database interactions.

AI Applications : Data Usage and Flow Monitoring

Maps end-to-end data flows for AI applications, including both internal models and third-party API calls.
Provides a holistic view of training data, vector DB interactions, and query logs, ensuring full transparency for AI applications.

Limited context on AI applications.

Anomaly Detection

Detects anomalies across data flow, access patterns, and user behaviour.

Limited to static anomaly.

Effortless, Real-Time Security

Aurva’s modern, eBPF-based architecture offers significant advantages over traditional DB-agent approaches like Imperva. It eliminates performance overhead, reduces deployment friction, and provides deep, real-time insights into data access without relying on costly audit logs. With flexible deployment models and context-aware alerting, Aurva reduces alert fatigue and accelerates time-to-value for security teams, making it a more scalable and efficient choice for hybrid and cloud-native environments.

Features

Zero Performance Impact

eBPF based architecture helps us monitor queries close to kernel providing zero latency (eBPF).

DB-Agents needs to be installed at DB Level often consuming 10% - 20% of the resource.

Time to Value

Easy, scalable deployment with native integrations across modern tech stacks, reducing time-to-value.

One at a time DB Agent installation often takes month. Scaling requires new servers to be commissioned often impeding the entire security

Cloud Native Support

eBPF-based for deep integration with the cloud infrastructure, collecting granular data at the OS kernel level with no-latency, real-time monitoring

Audit-log-based approach, relying on cloud provider logs to track database activities, costing an additional $100k/db/year

Flexible Deployment Model

Supports hybrid and cloud-native environments with eBPF/kernel agent deployments, ensuring rapid, low-overhead integration.
Agentless log-based approach (optimized by not pushing the logs on cloudwatch)

On-prem DB agents are not optimized for modern, distributed architectures, adding complexity to hybrid deployments.

Alert Fatigue

AI engine provides context-aware analysis to reduce false positives, factoring in database severity, connection context, and data volume to prioritize critical alerts.

Alert Fatigue to SOC team is common.

Cost

Cost-effective as it does not require enabling cloud service provider (CSP) audit logs, reducing operational costs.

Involves additional costs from enabling cloud audit logs, which increases the overall operational expense.
Imperva leads to additional infra cost of USD 10K+/DB when compared to Aurva's solution

Conclusion

Aurva: The Ideal Choice for Hybrid Environments

Aurva is designed for hybrid environments, providing deep, real-time visibility into data flows across both on-premise and cloud systems. Its eBPF-based monitoring offers granular insights without the need for traditional audit logs, ensuring low-latency, precise tracking of both machine and user activities.

With flexible deployment options and seamless integration into diverse infrastructure setups, Aurva delivers cost-effective, high-fidelity monitoring for organizations managing complex hybrid environments. Its lightweight, modern approach enhances security without adding unnecessary operational overhead, making it the ideal choice for businesses balancing on-premise reliability with cloud scalability.

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