AI adoption across enterprises is changing how data is created, processed and uploaded to AI tools for decision-making. These interactions may involve personal details, employee data, financial records or intellectual property, which could leave the organisation’s secure environment and become vulnerable to exposure.
Connecting AI bots to sensitive business systems such as CRM, ERP, data lakes, databases and file shares can improve productivity. However, greater connectivity also brings greater risk. Confidential information may be exposed when employees use unapproved AI tools or when these tools are connected to corporate systems without adequate safeguards. The risk is even greater for users with privileged access to sensitive HR, financial or intellectual property data.
Once AI tools are deployed, organisations need visibility into where sensitive data is stored, how it moves across tools, who is using it and for what purpose.
Traditional data discovery tools were not designed to provide this level of visibility into sensitive data and its movement. These AI tools derive value from unstructured content in emails, PDFs, presentations, images, etc. This access can expose sensitive information hidden in the documents. Traditional methods may therefore be unable to identify and enforce controls based on the context of the data.
Data Security Posture Management (DSPM) is a data-centric security approach that can uncover what kinds of sensitive data exist, where they are stored, who has access to them, which AI applications can consume them and what data is flowing into external AI repositories.
DSPM tools can monitor data across approved enterprise AI tools and identify risks associated with shadow AI, which refers to tools used without organisational approval or oversight. By scanning structured and unstructured data across cloud repositories and other systems, DSPM can identify intellectual property, financial information, personally identifiable information and other sensitive content.
DSPM can identify and classify sensitive data, while integrated Data Loss Prevention (DLP) and Cloud Access Security Broker (CASB) controls can govern file uploads to AI bots, prompt submissions and data sharing through cloud applications, helping prevent the unauthorised movement or leakage of sensitive data.
For example, source code may be exposed through AI coding assistants when it has not been classified as intellectual property. DSPM can identify and classify the code, while integrated controls can restrict its movement to and use by AI assistants. Similarly, if an employee pastes organisational data into an AI tool, access and enforcement controls can determine whether the information can be submitted.
A key limitation of traditional, rules-based DLP tools is their limited ability to interpret the broader meaning and business sensitivity of unstructured content. They typically rely on predefined patterns, keywords, regular expressions and existing classifications. Traditional tools may overlook confidential business documents without recognisable patterns or appropriate classifications. DSPM helps identify their sensitivity and how they are accessed or shared.
Once sensitive content has been identified and classified, DSPM can mask or redact it, block the submission, request justification or initiate an approval workflow before the document is displayed.
Managing access to data is equally important. Unlike traditional controls that rely on fixed allow-or-block rules, DSPM considers factors such as data sensitivity, user roles, access history and location. These insights help organisations make more informed access decisions.
In an AI-driven enterprise, data protection, incident response, business continuity and disaster recovery should operate as part of an integrated model. This model comprises four interconnected layers.The first layer focuses on discovering sensitive data, identifying where it is stored and who has access, and managing user rights. The second applies controls through DSPM, DLP and CASB, supported by measures such as encryption and data masking. The third enables organisations to enforce policies, continuously monitor data use and prevent misuse or unintended exposure once sensitive data has been identified and the appropriate controls are in place. The fourth activates isolation and containment measures when an incident occurs, followed by recovery processes that support business continuity. Insights from the incident can then be used to strengthen controls and improve standard operating procedures.
DSPM strengthens the first two layers by improving the discovery and classification of sensitive data and providing greater visibility into access, exposure and AI-related data flows. Integrated DLP, CASB, identity and information protection controls can then use these insights to govern how sensitive data is accessed, uploaded or shared.
Visibility across these layers helps security teams investigate affected data, users, permissions and systems, contain the impact of an incident and prioritise recovery. The resulting insights can inform policy updates, access reviews and improvements to standard operating procedures.
DSPM should not be viewed as a standalone control or a replacement for DLP. Its value lies in providing the contextual visibility needed to make existing data protection, enforcement and response controls more effective. By connecting data discovery with prevention, incident response and recovery, organisations can establish guardrails across the data lifecycle and adapt them as data use and risks evolve.
Authored by Dhruv Khanna, Partner, Deloitte India
