August 21, 2025

Guest Author, Ruchita Patankar, Content Marketing Manager, Cyera

In today’s AI-fueled, data-driven landscape, organizations are navigating uncharted waters. Generative AI (GenAI) has redefined how we build, analyze, and automate – but it’s also radically accelerated the sprawl, exposure, and misuse of sensitive data. For data security professionals, this isn’t just a new chapter; it’s a new playbook.

The challenge is twofold: while data has always been an asset to protect, AI turns it into both the input and output of the system. Sensitive records, source code, intellectual property, and personal information are increasingly fed into learning language models (LLMs), often without clear visibility or policy boundaries. This dynamic makes traditional security approaches, like siloed data loss prevention or periodic classification scans, highly inadequate. They just can’t keep pace with the volume, velocity, or variability of modern data use.

A prime example in today’s world is how AI tools are integrated across business functions. Developers feed proprietary code into chatbot windows for debugging. Marketing teams enter customer data into external models to generate insights. Even security teams use GenAI tools for threat modeling. Every one of these workflows risks inadvertently leaking sensitive or regulated data outside the organization’s control.

AI Adoption Starts with Data Visibility

As organizations move forward with AI adoption, they are rethinking their data security. This is where modern Data Security Posture Management (DSPM) and modern Data Loss Prevention (DLP) solutions come into play. Not as isolated tools, but as complementary halves of a complete data security strategy.

DSPM has quickly become indispensable in AI-era cybersecurity programs. DSPM platforms provide continuous discovery and classification of data across cloud environments, SaaS platforms, and on-prem. Rather than relying on one-time scans or static tags, DSPM operates in real-time, tracking where data is stored, how it moves, who touches it, and what level of risk it carries. Critically, they’re able to surface shadow data – locations that traditional tools would miss entirely.

On the other side of the equation, modern DLP has evolved beyond simple pattern matching. Today’s advanced DLP solutions are context-aware, leveraging behavioral signals and sensitivity labels to make dynamic enforcement decisions. And because these tools integrate across cloud apps, developer tools, and even browsers, they can apply controls across a variety of workflows. 

Together, DSPM and DLP form a powerful closed-loop system. DSPM feeds real-time insights into data sensitivity and risk posture into the DLP engine, which enforces controls across every possible egress channel. When DLP detects a policy violation or risky behavior, it feeds that signal back into DSPM, enriching the risk model. This feedback loop not only strengthens detection but also improves response time, reduces false positives, and aligns the security stack around real-world usage patterns.

For practitioners, this integrated approach provides something we haven’t had before: clarity. With DSPM, you know where your sensitive data is. With DLP, you control where it’s going. And together, they enable scalable, enforceable guardrails for how AI is used, without stifling innovation.

Purpose Built Data Security for AI

As regulations and emerging U.S. state AI laws begin to demand rigorous oversight of data used in and by AI systems, the ability to prove data lineage, enforce policy, and automate risk reporting is becoming a board-level concern. Security teams that simultaneously implement DSPM and DLP are not only safeguarding their organizations, they’re building a foundation for compliant, resilient, and trustworthy AI adoption.

As AI adoption increases, AI-SPM (Security Posture Management) is the category that has emerged. AI-SPM tooling discovers AI assets, maps model and data pipelines, assesses configuration and supply chain risk, monitors usage, and enforces policy across the AI lifecycle and cloud infrastructure. These capabilities are increasingly mission-critical as AI and agentic workflows are integrated throughout organizations.

This is where offerings such as Cyera’s AI-SPM come into play. Capabilities such as AI Runtime Protection, can detect and respond to threats in real-time, work alongside DSPM and DLP products to give you a holistic data centric view of your AI security. 

In a world where data is currency and AI is the accelerator, the best defense is no longer a patchwork of legacy controls. It’s a synchronized, intelligent strategy that sees everything, adapts in real time, and keeps your most valuable assets exactly where they belong; secure, compliant, and under control.

To learn more about Cyera’s approach to data centric AI security visit:https://www.cyera.com/secure-ai-adoption.