Less than five years ago, one would have been hard-pressed to find information on Data Security Posture Management (DSPM). Today, it is a central pillar of modern cybersecurity strategies. The growth of DSPM from a relatively new technology to a must-have capability of the cybersecurity landscape reflects the growing need for organizations to protect and control their data in the face of increasing complexity in the cloud and the broadening use of AI in the enterprise.

DSPM morphs into cloud-native DSP

Artificial intelligence (AI) continues to advance at a breakneck pace, and it’s rapidly transforming the systems it relies on, including the cloud. But at its core, data and AI are intrinsically linked — AI models fundamentally rely on data (and much of it!) to function. This amplifies the urgency for data security tools to evolve and meet the complex demands of AI. In addition to DSPM, this includes technology like data discovery and classification, data access governance (DAG) and data detection and response (DDR). The integration of these technologies forms the foundation of a holistic new data security platform (DSP). And it must work well for data everywhere — in the cloud or on-premises.

While DSPM remains a critical element in addressing existing data security challenges, a more comprehensive integrated data security approach is needed to meet the dynamic, AI-driven needs of modern enterprises. The development of generative AI, the growth of the cloud, and the tightening of regulatory and governance requirements have created new challenges that DSPM alone cannot adequately address. What’s needed is more comprehensive data security.

Enter integrated, cloud-native DSPs. These are the next generation of platforms with additional security features like access governance, threat detection, and data loss prevention, all designed to work seamlessly and scale with the cloud. External forces driving the further need for DSP features include sophisticated cyber threats that aim at sensitive data and the complexity of regulatory compliance. 

The business case for a comprehensive DSP

Traditional disparate data security approaches lead to gaps in visibility and often don’t scale well. Cloud-native DSPs solve this by combining several data security functions into an ‘all-in-one’ platform that scales and is easier to manage. 

The shift to integrated platforms is not just technological; business needs are also driving it. DSPs meet the needs of modern enterprises who need to reduce operational complexity and associated cost, enhance the efficiency and effectiveness of their security team and ensure all policies are enforced uniformly across all data assets. Finally, they must continually ensure business innovation — the lifeblood of most organizations — is secure, particularly as they increasingly use AI.  

Next-generation DSP requirements

Enterprises considering implementing a DSP should look for three main capabilities in their next-generation data security platform:  

1. First, they should provide context-aware data discovery and classification that uses AI to precisely understand what data exists and its business relevance and sensitivity. This awareness is the first step in making accurate risk assessments and policy implementations. A DSP must also prioritize scalability to handle growing data volumes and evolving business needs, ensuring consistent, efficient performance and adaptability as the organization expands.
2. Second, a DSP should offer real-time threat detection and response capabilities that complement the conventional posture assessments and incorporate continuous monitoring and automated remediation of security threats. This mitigates risks more quickly before damages can occur.
3. Third, a DSP should integrate seamlessly with the security ecosystems to enable coordinated security measures and consistent policy enforcement across all data assets. This means compatible labeling systems and easy customization to fit unique customer requirements.

Looking ahead: The future of data security

Data has become the lifeblood of nearly all businesses. Data-driven organizations, that realize the increasingly crucial role that data plays in their value creation, must take a new approach to secure and control  this most valuable asset.

With data democratization, rapid cloud adoption, and inter-connected supply chains, organizations should enhance their security strategy to protect data everywhere — no matter where it travels. This will empower their innovators to securely utilize data in new ways — to drive the business and improve customer outcomes — while adhering to the highest standards of data privacy and regulatory compliance.

Further, AI will continue to rise, bringing both new security risks and new protection solutions. Organizations will require platforms to secure AI training data while using AI in threat detection and response. Additionally, cross-cloud data governance will continue to grow as more organizations adopt multi-cloud strategies. Platforms will need to provide consistent visibility and control across all environments.

Finally, automation will play a more significant role in security operations; platforms will offer more sophisticated automated remediation capabilities and integrations to key security workflow tools to manage the growing complexity and burden of securing data.

As bright as the future can be, many are stuck with the past. For progressive organizations (the ones likely to prosper in the AI era!), this shift to comprehensive data security will become ingrained within their data architecture — and not be an added on afterthought.

Mastering data isn’t just a strategic advantage — it’s the foundation of survival in today’s competitive market. Data is the way to success and prosperity in an organization. Forward-thinking organizations use comprehensive DSPs to navigate this new era where speed, innovation and security can finally coexist.