3 Key Predictions for DSPM in 2025: The Future of Cloud Data Security

In 2025, the landscape of cloud data security will be incredibly dynamic and critical. As businesses increasingly migrate data to the public cloud, the need for robust, forward-thinking data security has never been more urgent. This shift will be driven by a myriad of factors, from the proliferation of stringent data regulations to the rise of sophisticated, AI-powered, cloud-driven data breaches. Given these factors, organizations will need to adopt proactive strategies such as data security with zero trust frameworks and AI-powered data security posture management (DSPM) to stay ahead of evolving threats.

3 Trends to Watch in 2025 

Prediction #1: Expect a Massive Spike in AI-Driven Attacks Targeting Data

Description: In 2025, the cloud data security landscape will be shaped by an unprecedented surge in sophisticated and targeted cyberthreats. Cybercriminals will be leveraging advancements in artificial intelligence (AI) and machine learning (ML) to develop more effective and stealthy attack methods, making detection and mitigation a formidable challenge. 

From the automation of malware development to the insidious corruption of AI models within organizations, malicious actors will leverage AI to craft highly targeted and astoundingly convincing threats that can hit organizations at 100x speed and scale. Malicious actors will also use AI to automate phishing campaigns, create highly convincing deepfakes, and develop advanced malware that adapts in real time to bypass traditional defenses.

Priority: To tackle this year’s cloud data security challenges, organizations will need to embed advanced AI and ML-powered security measures that will help detect and neutralize emerging threats in real time. They will also enhance threat detection and response, threat prediction and hunting, and combine security posture management with behavioral analytics to help monitor and secure sensitive datasets in real time, spotting risks such as data exfiltration attempts or unusual data access patterns. 

AI and ML algorithms paired with advanced threat correlation will also help organizations identify and prioritize data risks, reduce alert fatigue, and help security teams focus their efforts on protecting the most critical data assets. AI will also automate time-consuming processes, allowing security teams to focus on high-impact work and help organizations stay secure without needing a large team. 

Organizations can also choose zero trust architectures. These architectures will make sure only authorized users and devices can access sensitive data, thus greatly reducing the attack surface. Simultaneously, organizations need to foster a culture of vigilance to protect their cloud data against a broad spectrum of threats and vulnerabilities.

Prediction #2: Cloud Data Security Will Become a Top Priority 

Description: In 2025, cloud data security will become crucial as more organizations move their operations, mission-critical apps and data to cloud environments. One of the most pressing issues is the security of data stored in the cloud, given its volume, variety, and velocity. According to IDC, data is expected to grow at a compound annual growth rate of 21.2%, reaching over 221,000 exabytes by 2026. 

As more and more mission-critical apps and services move to the cloud, sensitive information is constantly created, transmitted, and stored. Most of this data is insecure due to: 

• Limited visibility: Most security teams find it challenging to discover sensitive data, where it is located, who has access to it, or how it's being used leading to security blind spots and data exposure.
• Privileged access and entitlements by default: Security teams find it challenging to track data access permissions, enforce the principle of least privilege and strike the balance of making data accessible while ensuring data is safe and compliant.
• Public exposure: Configuration errors are common due to frequent changes in cloud services and configurations leaving sensitive data exposed and vulnerable, and it is crucial for security teams to fix these issues before they are exploited by bad actors. 

Priority: Organizations need to adopt a proactive approach to cloud data security that helps them accurately discover and classify data, prioritize risk, and apply necessary security controls. This includes implementing advanced security solutions like DSPM that provide continuous monitoring and risk assessment of cloud data environments. 

Prediction #3: The Rise of Agentic Generative AI and Retrieval-Augmented Generation (RAG) Will Put Enterprise Data Governance at Risk 

Description: Large language models (LLMs) models are progressively becoming more powerful. Models like OpenAI’s o1 provide significant capabilities like complex reasoning and better problem-solving capabilities. This is allowing organizations to push much beyond the typical conversational AI use cases for LLMs. The advent of advanced reasoning capabilities within foundational models and the introduction of retrieval-augmented generation (RAG) provides the perfect inflection point for data automation and advanced enterprise workflows. 

Generative AI workloads running in public clouds combined with enterprise data lakes like S3 or Azure Storage deliver significant capabilities, enabling AI workflows that feed off significant volumes of sensitive enterprise data. In addition, AI automation kernels like Semantic will open the doors for multi-model AI agents with the potential to span multiple cloud service providers, providing significant data governance headaches for security organizations.  

This will lead threat actors to focus on techniques such as prompt injection, which may expose critical business data.  

Priority: Organizations need to gain visibility on the sensitive data exposed to AI workloads and services. Discovery and classification of cloud data and gaining visibility of the AI blast radius is going to be a critical stepping stone for organizational AI data protection strategy. In addition, organizations need to ensure significant AI controls such as AI content filters, data encryption, prompt logging and network exposure of AI resources are implemented for effective AI data security posture management. 

Key Factors to Consider and How Organizations Should Prepare

To tackle ‌data security challenges in 2025, organizations need a broad set of data protection strategies, innovative yet comprehensive tools, and a complete focus on staying one step ahead of cybercriminals. With the right approach, organizations can transform these challenges into a competitive edge while safeguarding their mission-critical data assets. Here are some suggestions and best practices for organizations in 2025. 

Level up data security with DSPM: The most logical step is to have DSPM to extend data security coverage to the cloud. Organizations can expand coverage of their existing data protection suite to access data risks in the cloud with DSPM while keeping security coverage over data everywhere (cloud, web, email, endpoint, AI, and more). 

DSPM will help combat data sprawl challenges and enhance data security posture. It will provide comprehensive visibility, precise classification, inventory and granular control over cloud data, enabling organizations to meet the security and stringent data regulatory requirements that are becoming the norm.

Secure AI adoption with DSPM: Strategic use of AI services (e.g., Azure AI Foundry, AWS Bedrock, GCP Vertex AI) is paramount for organizations to tailor their own solutions. These services are given access to sensitive data storage services for learning and training purposes. 

Poorly configured services may inadvertently expose private data during user interactions or as part of their outputs. Organizations should look to prioritize securing:

1. Sensitive data that is shared with AI services
2. AI services configurations to prevent sensitive data exposure as bad actors are targeting AI services. 

Additionally, security teams need to closely monitor AI services, detect misconfigurations, identify and address vulnerabilities. Using the power of DSPM, security teams can effectively safeguard sensitive data, ensure compliance, and mitigate the risks associated with progressive technologies like AI services.

Implement zero trust: Security leaders need to focus and accelerate zero trust implementation, especially around data pillars, to prioritize data access management for human and non-human identities and prevent sensitive data exfiltrations or breaches. 

Consolidate the security stack: Consolidating your security stack and adopting a single, unified data security platform has benefits that can go beyond the total cost of ownership (TCO). Namely, it can drastically reduce the time to detect and respond to security incidents.

Facilitate collaboration among stakeholders: Business leaders, security, and governance, risk, and compliance (GRC) professionals must team up on data security. Sharing risk information across multi-disciplinary groups is critical for facilitating collaboration and staying agile in a rapidly evolving threat landscape.

Cloud Data Security 2025

Amidst the rapidly changing landscape of cloud data security, a complex interplay of risks and rewards is unfolding. Cloud data security will undergo continuous transformation in response to emerging trends, such as the widespread adoption of multicloud environments, evolving regulatory demands, and the rise of AI-driven targeted cyberthreats. 

For a deeper understanding on how Zscaler can help and empower your organization to navigate as well as elevate cloud data security, both today and tomorrow, we invite you to schedule a comprehensive and tailored 1-on-1 demonstration of our solutions that deliver real, actionable results.