DSPM in 2025: Key Trends Transforming Data Security

The idea that ‘data security is almost worthless without context’ always holds true. You can never be certain if you don’t have any insights into what data you have, how much of it is regulated, which users or identities can access it, or how it has transformed over time. For a reality check, a 2024 DSPM Adoption Report cites that 83% of IT and cybersecurity leaders assert that lack of visibility into data contributes significantly to weak security posture.

One of the core features of a data security posture management (DSPM) solution is its ability to provide deep visibility into sensitive data in cloud environments. Since its introduction in cybersecurity, DSPM has become the fastest-growing category–a must-have tool in every security leader’s tech-stack. However, as with any other technology, DSPM must evolve to meet the current era's demands.

This blog explores the top DSPM trends in 2025 shaping data security.

Top DSPM Trends in 2025 to Look Out For

Data Security Posture Must Extend to Ensure GenAI’s Safe Use

Large language models (LLMs) are taking the world by storm, empowering enterprises to drive innovation, increase productivity, and retain a competitive edge. While AI has infinite use cases and benefits, it introduces a great number of security challenges that must not be taken lightly.

For starters, GenAI’s ability to analyze and understand unstructured data has created opportunities for enterprises, as this data encompasses valuable insights. However, traditional data security measures, optimized for structured databases, do not apply to unstructured data.

Secondly, GenAI relies largely on an organization’s data for training, fine-tuning, and Retrieval-augmented generation (RAG). Preparing GenAI pipelines without knowing or protecting the data that flows through the models creates significant security, privacy, governance, and compliance risks. Consequently, these risks create barriers that impede the adoption of GenAI in enterprises.

These challenges and risks make it critical for DSPM solutions to go beyond conventional cybersecurity borders and ensure stringent security controls across data and AI-powered systems and applications. Robust DSPM solutions with AI security integration must discover sanctioned and Shadow AI across the environment and monitor their access to sensitive data. They must also prevent unintended access to sensitive or regulated data used for training or RAG. This will enable enterprises to mitigate security, governance, and compliance risks.

DSPM Must Address the Unique Requirements of Data & AI Laws

Data protection laws and regulations are designed to ensure the integrity of data, its fair use and processing, and, more importantly, to uphold users’ right to privacy. However, as AI has become a cornerstone of almost every new technology released into the wild, the need for more AI-focused regulatory guidelines continues to grow. With this in view, governments across the globe have proposed or enacted AI laws, such as the EU AI Act, the US Executive Order 14110, and the FTC AI guidelines, to name a few.

Take, for instance, the EU AI Act’s Article 10–Data and Data Governance. The article requires covered entities to train, validate, and test datasets according to best data governance and management practices. Datasets must use relevant data preparation practices like labeling, cleaning, sanitization, or aggregation. Covered entities are further required to adopt effective measures for detecting, preventing, or mitigating bias. Similar and more regulations regarding AI training data, model access entitlements, etc., can be found in various other AI laws.

The shift to AI and associated laws requires DSPM solutions to cover all these aspects and offer a robust, automated compliance feature. The feature may include autonomous compliance reporting and Data + AI risk assessments.

DSPM Should Detect & Mitigate Toxic Combinations

Google defines toxic combinations as “a group of security issues that, when they occur together in a particular pattern, create a path to one or more of your high-value resources that a determined attacker could potentially use to reach and compromise those resources.“

Take, for instance, a cloud environment that may have multiple security concerns, such as a misconfigured bucket, publicly exposed storage, unintended access to AI training data, etc. When viewed individually, these risks may seem manageable. However, when combined, they may create a catastrophic effect.

The need to formulate solutions to mitigate toxic combinations has grown massively recently due to AI. The technology has added an extra layer of complexity to an already complex IT infrastructure. This has further led enterprises to overlapping configuration issues, creating toxic combinations across environments. To put things into perspective, 82% of security leaders have shown concerns that AI will amplify toxic combinations.

Traditional solutions like SIEM generate too many alerts, making it difficult for analysts to filter through the noise and prioritize critical alerts. Moreover, alerts are often ignored due to a lack of data context. Toxic combinations help determine which risks are more critical and prioritize remediation efforts accordingly.

DSPM solutions need to adopt a graph-based approach to security to detect and remediate toxic combinations. The graph should provide insights into the relationship between sensitive data, systems, applications, or AI models, highlighting toxic combinations so they do not go unnoticed.

Interested in learning how Securiti’s DSPM solution can help you evolve your data security strategy? Check out our on-demand webinar, GigaOm DSPM Radar Highlights: Your Guide to Data+AI Security.