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How Data Security Posture Management tools like Concentric AI can help secure Copilot Deployment

March 13, 2024
Cyrus Tehrani
6 min read

In a business landscape dominated by rapid digital transformation and AI adoption, protecting all information — even data generated by AI — has never been more critical. Especially as organizations increasingly rely on AI and machine learning technologies to streamline operations and increase productivity, classifying and ensuring adequate access controls to sensitive data becomes paramount.

Microsoft’s advanced AI assistant, Copilot, is already being introduced in many corporate environments and is changing how organizations interact with data across Microsoft 365 applications.

However, robust data security posture management is an essential pre-requisite prior to deploying Copilot to ensure that organizations balance Copilot’s productivity increases with protecting sensitive data.

The importance of data security posture management for AI usage

What is Data Security Posture Management? 

DSPM enables organizations to gain a clear view of the where, who and how of their sensitive data: where it is, who has access to it, and how it has been used.

DSPM empowers organizations to:

Discover sensitive data: Gain comprehensive visibility into where sensitive data resides and the type of sensitive data that exists across cloud environments.

Classify data: Tag and label sensitive data.

Monitor and identify risks: Proactively detect and assess risks to business-critical data, preventing potential breaches before they occur. Risks may include fixing permissions, changing entitlements, stopping risky sharing, and moving data to the right location.

Remediate and protect: Implement robust measures to protect sensitive information against unauthorized access and data loss.

What this means is that sensitive data must be identified and classified. The key is to take all the data organizations care about and classify it accordingly. This way, as data moves through the network and across structured and unstructured data stores, it is labeled appropriately no matter where it resides. Then, it can be monitored for risk such as inappropriate permissions, risky sharing, inaccurate entitlements, wrong location etc.

If any risks are detected, they can be remediated.

Copilot data security risks

While Copilot introduces a new horizon of possibilities, it also brings forth challenges related to data access and security.

Copilot suffers from 4 key security issues:

1. Copilot output inherits sensitivity labels from input, which means if data is not classified correctly, the output will also be incorrectly classified.

Real world scenario: A financial analyst uses Copilot to generate a quarterly financial report. The input data includes a mix of public financial figures and sensitive, unreleased earnings data. Due to an oversight, the sensitive data is not correctly classified at the input stage. Then, Copilot generates a comprehensive report that includes sensitive earnings data but fails to classify it as confidential. What if this report was inadvertently shared with external stakeholder?

2. Copilot inherits access control permissions from input, and the output therefore inherits these permissions. This means that if you have data with inappropriate permissioning, sharing and entitlements, the output will also suffer from the same issues and lead to potentially devastating data breach or loss. Concentric AI’s Data Risk Report shows that far too many business-critical files are at risk from oversharing, erroneous access permissions and inappropriate classification — and can be seen by internal or external users who should not have access.

Real world scenario: An HR manager uses Copilot to compile an internal report on employee performance, including personal employee information. The source data has overly permissive access controls, allowing any department member to view all employee records. What if the Copilot-generated report inherits these permissions, and sensitive employee information becomes accessible to all department members, violating privacy policies? It could potentially lead to internal chaos and legal challenges.

3. Company context on sensitivity is not factored into output. Every company has sensitive data such as financial records or intellectual property or business confidential customer data. Copilot is unlikely to factor this context into its decision making around output or who should have access to it

Real world scenario: A product development team uses Copilot to brainstorm new product ideas based on existing intellectual property (IP) and R&D data. The team’s input includes confidential information about upcoming patents. Copilot, lacking context on the company’s sensitivity towards this IP, incorporates detailed descriptions of these patents in its output. What if this output is then shared with a broader audience, including external partners, inadvertently exposing future product plans and risking IP theft?

4. By default, Copilot output is unclassified, which means output that may be potentially sensitive could be accessible by anyone.

Real world scenario: A marketing team uses Copilot to analyze customer feedback and generate a report on customer satisfaction trends. The input data contains sensitive customer information, including criticism of unreleased products. Since Copilot outputs are unclassified by default, the generated report does not flag the sensitive customer feedback as confidential. What if the report is uploaded to a shared company server without appropriate access restrictions, making the critical feedback—and details about the unreleased products—accessible to unauthorized employees? Internal leaks and competitive disadvantage become a significant risk.

How Concentric AI addresses Copilot security risks: Key benefits

Concentric AI manages these risks with sophisticated natural language processing capabilities to accurately categorize data, including outputs from Copilot. With our Data Security Management solution, sensitive information is correctly identified and protected, addressing potential security risks without compromising productivity.

Here’s how Concentric AI addresses the four security challenges organizations face before, during and after a Copilot deployment.

1. Incorrectly classified output due to inherited sensitivity labels

Concentric AI can mitigate the risk of incorrectly classified outputs by implementing advanced data discovery and classification processes that automatically identify and classify data based on its content and context before it’s input into Copilot. By ensuring that all data is accurately classified at the source, DSPM prevents incorrect sensitivity labels through Copilot’s outputs from being propagated. Concentric AI can also continuously monitor data flows, reclassifying data as necessary and ensuring that any data processed by Copilot, and its subsequent outputs, maintain the correct classification levels.

2. Inappropriate permissioning, sharing, and entitlements

Concentric AI addresses the challenge of inappropriate permissioning by providing granular visibility into data access controls and entitlements across the organization’s data stores. It can automatically assess and adjust permissions based on the data’s classification, ensuring that only authorized users have access to sensitive information. Before data is processed by Copilot, Concentric AI can enforce the principle of least privilege, correcting over-permissive access settings and preventing sensitive outputs from being inadvertently shared or exposed. This proactive approach to permissions management significantly reduces the risk of data breaches and loss.

3. Lack of company context in output sensitivity

To solve the issue of missing company context in sensitivity assessments, Concentric AI leverages sophisticated NLP and machine learning algorithms to understand the nuanced context of data, including its relevance to specific business processes and its sensitivity level. By integrating DSPM with Copilot, organizations can ensure that the AI tool is informed about the company-specific sensitivity context, providing a blueprint for Copilot as it factors in this critical information when generating outputs. Concentric AI ensures that sensitive data, such as intellectual property or confidential business information, is handled appropriately, maintaining confidentiality and integrity.

4. Unclassified outputs making sensitive data accessible

Concentric AI directly addresses the challenge of unclassified outputs by automatically classifying all data processed by Copilot, ensuring that outputs are immediately tagged with the appropriate sensitivity labels. This automatic classification extends to Copilot-generated content, ensuring that any sensitive information contained within these outputs is immediately recognized and protected according to its classification. By enforcing strict classification protocols, Concentric AI ensures that sensitive outputs are not inadvertently accessible, maintaining strict access controls based on the data’s sensitivity and compliance requirements.

The final word

The full potential of Copilot can be unlocked with Concentric AI — we empower organizations with well-classified, accurately categorized and appropriately permissioned data.

The key takeaway here is that when it comes to deploying any type of AI tool like Copilot, deploying Concentric AI is helpful before, during and after deployment. The risk to sensitive data is high enough without Copilot in the mix; adding it blindly only amplifies that risk to a level that’s far too uncomfortable.

Want to see — with your own data — how Concentric AI can help ensure a secure Copilot rollout?

Concentric AI is easy to deploy — sign up in ten minutes and see value in days.

Contact us to book a demo today.


Libero nibh at ultrices torquent litora dictum porta info [email protected]

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