The Risks of Legacy DLP: Why Cloud Security Needs DSPM

82% of 2024 data breaches involved cloud data, raising concerns about the effectiveness of legacy data loss prevention (DLP) solutions in today's cloud-centric data environment. These traditional solutions often struggle due to limited visibility and control in multi-cloud data environments. Designed for on-premises infrastructure, traditional DLP lacks the necessary APIs and integrations to scan data across diverse cloud platforms like AWS, Azure, and Google Cloud, leaving shadow data and misconfigured repos vulnerable. In contrast, DSPM solutions offer discovery, classification, continuous monitoring, automated risk assessments, and remediation, significantly mitigating data breach risks.

Read on to discover how a modern approach, Data Security Posture Management (DSPM), is revolutionizing cloud data protection and addressing the limitations of traditional solutions.

Rethinking DLP: The Need For DSPM In The Cloud

Traditional Data Loss Prevention (DLP) solutions were initially designed for a time when most enterprise data resided on-premises and moved primarily between endpoints. However, the digital landscape has undergone a significant transformation, with the majority of organizational data now residing in the cloud. Modern enterprises operate within complex, highly distributed data environments encompassing public and private clouds, SaaS applications, cloud data lakes, and data warehouses. The widespread adoption of hybrid and multi-cloud environments, cited at 82% in Cisco's 2022 Global Hybrid Cloud Trends Report, underscores this shift. Consequently, legacy DLP approaches struggle to effectively secure these dynamic and dispersed data assets.

Pitfalls of Traditional DLP

DLP solutions primarily monitor endpoint activity for potential data exfiltration. Their effectiveness is often hindered by inaccurate data classification and labeling, resulting in a high volume of both false-positive and false-negative alerts. The reliance on outdated Regex-based classification techniques contributes to these inefficiencies, resulting in alert fatigue and an increased risk of undetected sensitive data leakage.

Furthermore, in contemporary cloud environments, DLP tools lack the ability to monitor sensitive data exfiltration. They cannot evaluate the exposure risks associated with data sitting in a cloud data store, sensitivity levels, and access misconfigurations. Continuously monitoring and ensuring compliance with industry standards and data privacy regulations is also not the forte of legacy data security solutions.

According to IBM’s 2024 Cost of a Data Breach report, 82% of all data breaches involved cloud-stored data, indicating that the cloud is a significant target for attackers. 15% of the breaches involved cloud misconfigurations as the initial attack vector. The same report states that the global average cost of a data breach reached $4.88 million in 2024, with cloud-related breaches accounting for 45% of the total cost.

Hence, organizations relying on DLP as their primary data security tool in the cloud & AI era are at high risk. The limitations of legacy DLP result in significant gaps in visibility regarding the location of sensitive data in the cloud, as well as a lack of adequate controls for effective data protection. Consequently, organizations face heightened vulnerability to data breaches, financial repercussions, and reputational damage due to a weakened cloud data security posture.

Cloud Data Security With DSPM

The transition to cloud computing has introduced a distinct set of data security challenges, necessitating the adoption of modern solutions. Data Security Posture Management (DSPM) has emerged as a proactive and contemporary approach to safeguarding critical data assets. Securiti’s DSPM provides comprehensive visibility into sensitive data, including its location within the enterprise, user access privileges, and data usage patterns. It delivers valuable insights into the overall security posture and associated risks, enabling organizations to implement robust controls and policies for effective risk mitigation. In essence, DSPM enhances data security and privacy, ensures compliance with data regulations, and addresses the limitations of traditional DLP solutions.

Gartner first introduced and defined the term DSPM in its 2022 Hype Cycle™ for Data Security report. GigaOm further elaborated on this definition in its 2024 GigaOm Radar report, describing DSPM as a solution that offers

"visibility into where sensitive data is, who has access to it, and how it is being used. DSPM gives a comprehensive view of an organization’s data security posture, its compliance position, security and privacy risks, and, crucially, how to deal with them.”

Governing AI Data With DSPM

The proliferation of generative AI is fundamentally changing how organizations create and utilize data. AI models depend on high-quality data, and the practice of feeding substantial datasets into these models has become common. This introduces new data security risks, as organizations may inadvertently expose sensitive information to AI agents and copilots. Legacy DLP solutions are ill-equipped to monitor data flows into AI pipelines, exacerbating this risk. A Deloitte survey revealed that 58% of organizations are concerned about feeding sensitive data to AI models, reinforcing the inadequacy of traditional approaches in the current cloud and GenAI landscape.

Securiti’s DSPM offers solutions to these emerging challenges by enabling organizations to discover cloud-native data used by AI models, thereby facilitating the governance of sensitive data in AI model fine-tuning or Retrieval-Augmented Generation (RAG). It also aids in enforcing least privilege access to prevent excessive data sharing with AI users. Moreover, DSPM helps identify and manage redundant, obsolete, and trivial data stored in forgotten repositories, thereby improving the quality of AI outputs.