Data Access Intelligence & Controls

,p>Gartner highlights that “data and analytics leaders who share data externally generate three times more measurable economic benefit than those who do not.”

Data drives business, growth, and innovation. However, to achieve those grand business objectives, data needs to be shared across departments. Users and systems (including AI agents) need to have seamless access to data to make the most of the shared knowledge.

However, it is also imperative to remember that organizational data often includes sensitive or confidential information. If that data falls into the wrong hands, it could lead to catastrophic outcomes like legal penalties, reputational damage, and loss of customer trust.

In fact, industry reports highlight that more than half of the data breaches involve the abuse of access privileges.

Typically, cloud services offer native identity and access management (IAM) capabilities for their respective environments. These tools offer granular visibility into roles and permissions, i.e., which users and roles are accessing what data and which systems.

The challenge arises when access governance teams need unified data access visibility. Since every tool operates in their environments in silos, getting a comprehensive view under one roof becomes difficult. More importantly, simply having access visibility isn’t enough for robust data security.

Security teams need context for sensitive data access. This means they want to understand what data assets are risky, i.e., containing sensitive PII of customers or employees. Another critical insight that needs to be identified is the regulatory context of the data, i.e., what access patterns may violate privacy regulations like GDPR, CPRA, EU AI Act, etc.

DSPM gives organizations a contextual understanding of data access across environments. The tool visualizes access entitlements by ingesting and unifying IAM metadata from different environments, providing a comprehensive knowledge graph. This visualization shows access governance teams who have access to sensitive data, how the sensitive data is being accessed, how the permissions have been granted, and who has administrative control.

These valuable insights help teams gain granular details about access visibility so they may remediate risky access patterns effectively and promptly. Apart from access visibility, DSPM further allows teams to identify suspicious or abnormal access patterns. For instance, a user accessing large volumes of data than usual is triggered as an anomaly. By understanding suspicious access patterns, teams can effectively identify and remediate over-provisioned users or roles. This allows teams to enforce a least privileged access model by right-sizing entitlements.

Another core feature under the access intelligence and controls capability is the ability to define and enforce policy-driven entitlements. Administrators can define granular entitlements down to the very file or column level, governing privileges like select, modify, or both for structured and unstructured data.

Overall, DSPM provides a comprehensive and scalable framework that leverages access intelligence, user behavior, and policy-based controls for secure and seamless data sharing.

Moreover, AI has heavily dominated today’s digital landscape. The level of intelligence that DSPM offers can greatly help with AI security and governance. It does so by mapping the relationship between AI models, AI agents, or Copilots, and managing entitlements. This helps organizations ensure that only authorized users can access specific data via prompts. For instance, if a user enters prompts asking for access to some finance data via MS 365 Copilot while the user doesn’t have access to that data, the Copilot would honor the access entitlement by excluding that data.