Announcing Customer Success, New Platform Capabilities to Enable Safe Use of LLMs and Data Lakes + RSA Highlights. Read Both Announcements.

FEATURED

Gartner® Innovation Insight: Data Security Posture Management
PLATFORM
The Normalyze Platform
Supported Environments
Platform Benefits
USE CASES

Reduce Data Access Risks

Enforce Data Governance
Eliminate Abandoned Data

Secure PaaS Data

Enable Use of AI

DSPM for Snowflake

MARKETS

Healthcare
Retail
Technology
Media
M&A

FEATURED

DSPM-chat-Richard Stiennon-Ravi-Ithal-Normalyze
Improve Cloud Security:
Dark Reading Interviews Ravi Ithal

FEATURED

CYBER 60: The fastest-growing startups in cybersecurity

What’s New in

Normalyze

Abandoned Data Explorer™

Normalyze’s insights reveal that over 70% of data backups have been neglected for over a year, and alarmingly, 15% have lingered unattended for three years or more. Not only do these abandoned data stores lead to escalating storage costs, but they also present a severe risk profile, as unmonitored and outdated data can easily become a target for cyber threats.

The Abandoned Data Explorer enables teams identify and manage abandoned or stale data stores, including backups and snapshots. Get detailed insights into the space used in your backups and the storage tiers used to store them, so you can prioritize remediation for the abandoned backups with highest usage. A customer reported finding abandoned data that was costing them nearly $18k a month to store.

frame for cases

Snowflake Native App for DSPM

Snowflake customers can now apply advanced tools for automated data discovery and classification, precise access management via the Data Access Graph, and proactive risk monitoring to enhance their security measures across their Snowflake data landscape and ensure compliance with regulatory requirements.

Using Normalyze’s native integration with the Snowflake Data Cloud, customers can now seamlessly secure their data using Snowflake Horizon’s security and compliance capabilities in leveraging the Normalyze patented Data Security Posture Management (DSPM) platform for data discovery, classification, access governance, risk management, and compliance.

DSPM for AI

Generative AI and Large Language Models (LLMs) are introducing significant data security challenges: without proper data classification, there’s a real risk that these systems might unintentionally process and expose sensitive or valuable information. The challenge is compounded by the proliferation of ‘shadow AI’— technologies deployed directly by business teams without IT oversight.
Normalyze now offers capabilities to scan, identify and classify sensitive data in training data sets; and it offers specialized APIs to conduct real-time sensitivity analysis of data going into and out of LLMs.
Protect the data layer with Normalyze AI security capabilities.

Expanded DSPM for On-Premises

Many teams grapple with large amounts of data still present in non-cloud data centers. To help teams get visibility and control of sensitive data in these data stores, Normalyze supports both self-managed as well as cloud-based deployments of scanners to scan on-premises data, including on-premises databases and network and Windows file shares.

New Workflows to Validate Classification and Sensitive Data

New Validation Workflows give finer control to manage and optimize data security operations, with the capability to validate data classifications and entities at a granular level—either object or snippet—that directly impact the alerts and dashboards.

Prioritize Critical Security Risks and Data: Through Normalyze’s Validation Workflows, teams can target scanning and classification on specific, high-priority data elements at the object or snippet level, ensuring focus on genuine threats and reducing non-critical alerts. That means you can target scanning on known high-importance data and ignore known low-risk data, thereby reducing alert noise and improving operational efficiency.

Tailor Protection to Fit Organizational Needs: These workflows empower teams to customize how data is classified and flagged within the platform, adjusting alert sensitivity and specificity according to the unique needs and priorities of the organization. By fine-tuning how data is handled at the snippet or object level, teams can ensure that the most relevant and significant data points are highlighted in security alerts and dashboards, streamlining investigations and ensuring that security measures are directly aligned with organizational strategies.

Selectively ignore any profile for a single instance across the whole file or the whole data store.

DEMO

Discover, visualize, fix

Get a full picture of your sensitive data landscape and discuss your use case live with a security engineer.