Top Tips for Protecting Private Data in Financial Services
Data security is challenging for every business, but if you’re in finan...
Note: this article has been updated & refreshed as of 7/25/2025
As cloud adoption skyrockets alongside the surge in the sheer amount of data organizations share, managing access to that data can be overwhelming. While cloud makes collaboration and sharing easier than ever, data discovery and risk assessments are even more difficult.
All organizations, especially regulated enterprises, have obligations to strictly control employee access to private, sensitive data. Many other corporate activities – such as mergers and acquisitions – also need robust internal information barriers.
Unfortunately, sensitive data is often embedded in places that are both hard-to-find and hard-to-manage — like financial analysis spreadsheets or business forecast reports.
Plus, according to a recent Identity Theft Resource report, 80% of enterprises have experienced some sort of incident involving unauthorized data access.
Which raises this question: How can organizations find, assess, and control business-critical information to build effective information barriers between internal groups?
The answer is data access governance.
What is data access governance?
Data access governance (DAG) is the process of defining and enforcing policies for how data is accessed and used. It includes defining roles and responsibilities for those who have access to data, setting up processes for granting and revoking access, and auditing data access.
Data access governance also plays an essential role in ensuring that data is used appropriately and protected from unauthorized access, which can be a significant driver of data loss.
On a broader scale, DAG may also include risk assessments, privacy policy implementation, deploying access controls, discovering and classifying sensitive data, and monitoring access to critical assets. Also critical to data access governance is network traffic analysis, security awareness training, and staying current about new threats, tools, trends and compliance requirements.
According to Gartner, “Data Access Governance (DAG) solutions have now become critical as they provide data access assessment, management and real-time monitoring for the unstructured and semi-structured data…DAG solutions help to provide the right level of access for users/groups. DAG also helps customers migrate some of their data repositories from on-premises to the public cloud by classifying data and cleaning up permissions beforehand.”
Gartner®, “Hype Cycle for Data Security, 2021”, Brian Lowans, July 27, 2021.
A word about cloud data access governance
In the context of this article, cloud data access governance can be used interchangeably with DAG. Unlike traditional on-premises models, cloud governance also much account for cloud-related issues like dynamic permissions, third-party integrations, and ever-expanding datasets across multiple platforms.
What about AI data governance?
GenAI is already in your enterprise. Employees are using it to create content, it’s transcribing your meetings, it’s rewriting your documents… the list is endless. Today, it’s quietly circulating sensitive data through tools most of your security stack can’t see.
With new AI tools being introduced every day, your GenAI risk is harder to control than ever. It’s not just about security Copilot anymore.
While there are numerous risks of using GenAI , these are the four big one that you need governance for:
- It surfaces data that users shouldn’t’ see
- It generates new unlabeled content that creates fresh risk vectors
- Users can share sensitive data with public GenAI, which may get ingested into its model
- It often undermines compliance with GDPR, HIPAA, PCI, etc.
Without a governance strategy, you’re missing the controls that ensure integrity, accountability, and security while compliant competitors are surging ahead.
How does data access governance work?
The first step in the DAG process is classifying the data, which involves comprehensive data discovery, categorization and risk assessment.
Second, data access governance allows organizations to meet regulatory mandates for information barriers and access governance, and helps establish zero-trust data access controls to prevent customer data loss.
The next, and perhaps most important, step is to remediate any risk to the data.
With robust data access governance, organizations should be able to answer these questions:
- Where is my business-critical content?
- Is my sensitive data being shared only with those who are authorized to see it?
- Has data been shared or accessed inappropriately?
What are the benefits of data access governance?
If there’s one universal benefit of data access governance it’s this: it enables organizations to maintain data security by controlling and monitoring access to their data.
More specifically, here’s a closer look at three key benefits of DAG:
Stronger regulatory compliance
As the remote and hybrid work model continues to dominate how employees work and promote more data-driven operations, compliance regulations become more crucial. With data access governance, organizations can improve security and compliance while limiting their risk of regulatory fines.
Better risk management
With robust data access governance, your IT and security teams can gain the confidence that goes along with knowing that:
- Sensitive data is not exposed to employees, systems, or third parties that lack proper authorization
- There is a lower risk of security breaches from external malicious actors
- Insiders won’t be able to access data they have no rights seeing
Better access to data
Strong data access governance enables users and employees to not only get access to more data, but access to more relevant data that explicitly applies to their role. With data access governance, this data democratization can actually have a positive impact on organizations.
What is the difference between Data Security Governance and Data Access Governance?
Data Security Governance (DSG) and Data Access Governance (DAG) are often mentioned together and may be perceived as interchangeable, but it’s important to understand how they differ in the data management framework.
Data Access Governance (DAG) focuses on managing who has access to what data, and enforces policies that ensure only authorized users can access sensitive information. Think permissions, roles, and managing user access to mitigate risk of data misuse.
Data Security Governance (DSG), on the other hand, encompasses broader policies and practices aimed at protecting data itself. DSG includes access management but also covers encryption, risk management, data integrity, monitoring for threats, and complying with diverse regulatory frameworks. DSG ensures that data remains secure throughout its lifecycle, regardless of where it is stored or accessed.
Look at it this way: DAG is a subset of DSG—while DAG is about managing access, DSG is about the comprehensive security and protection of data.
How does Concentric AI help with data access governance?
Concentric AI is an easy-to-deploy SaaS security solution that provides organizations with agentless connectivity to a wide variety of data repositories so you can govern access to your data — wherever it resides.
Concentric AI’s powerful deep learning technology improves data access and activity governance by giving you an unparalleled contextual understanding of your structured and unstructured data. The solution can help enterprises remediate data vulnerability issues, meet access and activity governance regulations, demonstrate control to auditors, and implement zero–trust access practices – all without complex rules or relying on end-users.
Concentric AI’s advanced deep learning capabilities go beyond rules, pattern matching or folder-based controls to protect against careless sharing, compromised users, or insider threats.
Our solution improves data access governance across all your data stores by connecting to on-premises unstructured data storage, structured databases, and messaging and email applications. This means the solution can discover, categorize and monitor data wherever it is stored (and sprawled).
How does Concentric AI’s solution work?
Concentric Al’s Semantic Intelligence platform facilitates a robust zero-trust approach to data access governance where your users only have access to what they need, and every attempt to access is verified.
The platform’s deep learning capabilities allow it to understand the context of every data record, which results in far more accurate classification and access policies.
It flags issues such as sensitive data that is accessible to your entire organization or to external users, lack of classification or incorrect classification, excessive permissions, risky sharing, and unauthorized access. And it does this without depending on complex rules or pattern-matching.
Once these risks have been identified, you can set access policies that align with regulatory requirements directly in the platform.
The platform will autonomously enforce these policies if sharing and access issues are found, or it will integrate with other security tools and cloud APls to address them.
Why not give us a try using your own data? Contact us today to see firsthand how data access governance can be easily deployed in your organization.