The Role of AI and ML in DSPM

The rapid adoption of AI is reshaping the data security industry, with AI applications and the enormous amounts of data they rely on introducing new risks. Generative AI in particular is challenging because these algorithms often have access to enormous amounts of corporate data.

AI security is fundamentally data security, yet many organizations are lacking the data insights necessary to prevent AI systems from inadvertently exposing, ingesting, or misusing information. Safely adopting AI requires a data-led approach to discovering data, assessing its risk profile, and enforcing appropriate access controls. 

Data security posture management (DSPM) has become the fastest growing security category because it addresses the unique data security challenges of modern infrastructure, data sprawl, and generative AI applications. 

This article explores how DSPM can help enterprises secure their AI data and applications:

1. Discover Sprawling AI Data

Data is growing at a rapid pace around the world, with the total expected to reach 181 zettabytes in 2025. A key driver of this growth is the adoption of AI and machine learning applications, which require enormous amounts of data for training and inferencing.

The adoption of AI has broadened the attack surface, with information residing in emails, chat logs, legal documents, and media files being fed to AI models. For example, Microsoft Copilot introduces data security risks because it uses diverse data sources like Sharepoint and OneDrive that often contain sensitive company information within some files.

As data sprawl grows, there is a need for a DSPM tool that can discover data across all environments in real time, from on-premise and cloud servers to SaaS and AI applications. This includes creating an inventory of ghost AI applications and data that security teams aren’t aware of yet.

2. Classify Unstructured AI Data

Generative AI has the ability to process vast amounts of unstructured data, including text, images, and videos. This means organizations are collecting enormous amounts of unstructured data and feeding it to generative AI models, which is causing a shift in focus from structured to unstructured data. 

However, unstructured data is much more difficult to categorize and historically less of a priority for data security solutions. Legacy data classification tools using manual methods and rigid regular expressions often lead to false positives or misclassified sensitive data, especially when these techniques are applied to unstructured datasets.

An effective DSPM tool can accurately classify unstructured data using automated techniques to identify sensitive data within massive datastores. This includes classifying sensitive information at the file level, which is crucial for securing AI tools that ingest corporate data.

3. Secure Generative AI Tools

As organizations integrate AI into their workflows, there is a greater risk of data exposure due to inadequate access controls and security policies. Moreover, many employees are now using generative AI tools like ChatGPT and DeepSeek on a daily basis without understanding the security implications. 

As a result, over-privileged generative AI tools are leading to the inadvertent exposure of sensitive data to unauthorized internal users, external vendors, and cloud providers. This means there’s an increased need for proper governance and access controls to prevent users from inadvertently inputting sensitive data into these AI tools.

DSPM can identify and view the context around non-human identities like generative AI tools. This will help determine what data these tools can access, and limit their access as much as possible to mitigate security risks and minimize the blast radius of an AI breach.

4. Streamline Privacy & Compliance

The widespread adoption of AI is adding complexity to  data compliance, and new requirements are constantly evolving with privacy regulations like the EU AI Act on the way.

AI increases the potential for compliance violations because it processes massive amounts of data, and organizations do not always have visibility into whether it has access to sensitive information. For example, AI tools can accidentally ingest restricted data like customer PII or financial information, which may violate GDPR, CCPA, or industry-specific regulations.

DSPM can provide contextual insights about data residency, retention, and protection measures to help prevent compliance issues when using AI. Deep data insights helps organizations ensure that access controls, encryption measures, tokenization, MFA, logging, and other security protocols are applied correctly based on the data's sensitivity and risk level.

How AI Is Improving DSPM

Although AI is introducing new data security challenges, it is also leading to improvements in the way DSPMs and other security tools combat cyber threats, ensure data privacy, and enforce regulatory compliance.

Improves Data Classification

Innovative DSPMs are using large language models (LLMs) and proprietary AI algorithms for data classification. AI can analyze complex data patterns and context to classify information more accurately than traditional methods, and do so at the speed and scale necessary to secure massive unstructured data sets.

In addition, unsupervised AI-powered classification can be used to learn and categorize unique corporate data as well. This enables enterprises to apply more granular and appropriate security measures based on their unique data landscape.

Enhances Risk Intelligence 

Leading DSPMs are also leveraging AI algorithms to deliver risk intelligence that reduces false positives, and in turn, alert fatigue. By better understanding the context around data — such as who is accessing the data and where it is located — AI-powered data security tools can provide more accurate risk intelligence. 

Fewer false positives gives security teams the confidence to implement more efficient and targeted actions to mitigate threats without compromising on security or disrupting business operations.

Accelerates Threat Detection

Some DSPMs have threat detection capabilities that leverage AI to continuously analyze data access patterns, permission changes, and more in real time to uncover anomalies. This can surface changes in data exposure, identify emerging vulnerabilities, and flag new threats as they arise. 

Automated AI algorithms can detect potential vulnerabilities faster than manual methods, and often produce fewer false positives. When AI-powered DSPMs deliver automated alerts that are reliable and timely, security teams can act faster to mitigate threats and reduce remediation times. 

Choose an AI-Native DSPM

In short, AI unlocks tremendous opportunities across nearly every industry, but it does come with new data security risks. By adopting a modern DSPM solution, enterprises can safely leverage their data and AI to drive their businesses forward.

Cyera is an AI-native data security platform with advanced capabilities for mitigating AI and machine learning risks:

• Automatically discovers data across all environments, AI tools, and data pipelines — and continuously scans for changes over time.
• Uses hundreds of auto-classifiers and proprietary LLMs to automatically classify data and determine its criticality with 95% precision.
• Enables granular data access governance based on a deep understanding of your unique data.
• Provides an identity module that identifies non-human AI tools that have access to sensitive data.
• Easily uncovers sensitive data within Microsoft 365 that’s accessible to AI tools like Copilot.
• Includes data detect and response capabilities that consolidate and correlate data risk signals that can be acted on immediately.

‍Schedule a demo to discover if Cyera meets your AI and data security needs.