How CISOs Will Evaluate DSPM in 2026: 13 New Buying Criteria for Security Leaders

We knew we were on a hot streak, and now it’s official.

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Sentra has been named one of CRN’s 10 Hottest Cybersecurity Startups of 2025. This recognition is a direct reflection of our commitment to redefining data security for the cloud and AI era, and of the growing trust forward-thinking enterprises are placing in our unique approach.

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This milestone is more than just an award. It shows our relentless drive to protect modern data systems and gives us a chance to thank our customers, partners, and the Sentra team whose creativity and determination keep pushing us ahead.

The Market Forces Fueling Sentra’s Momentum

Cybersecurity is undergoing major changes. With 94% of organizations worldwide now relying on cloud technologies, the rapid growth of cloud-based data and the rise of AI agents have made security both more urgent and more complicated. These shifts are creating demands for platforms that combine unified data security posture management (DSPM) with fast data detection and response (DDR).

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Industry data highlights this trend: over 73% of enterprise security operations centers are now using AI for real-time threat detection, leading to a 41% drop in breach containment time. The global cybersecurity market is growing rapidly, estimated to reach $227.6 billion in 2025, fueled by the need to break down barriers between data discovery, classification, and incident response 2025 cybersecurity market insights. In 2025, organizations will spend about 10% more on cyber defenses, which will only increase the demand for new solutions.

Why Recognition by CRN Matters and What It Means

Landing a place on CRN’s 10 Hottest Cybersecurity Startups of 2025 is more than publicity for Sentra. It signals we truly meet the moment. Our rise isn’t just about new features; it’s about helping security teams tackle the growing risks posed by AI and cloud data head-on. This recognition follows our mention as a CRN 2024 Stellar Startup, a sign of steady innovation and mounting interest from analysts and enterprises alike.

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Being on CRN’s list means customers, partners, and investors value Sentra’s straightforward, agentless data protection that helps organizations work faster and with more certainty.

Innovation Where It Matters: Sentra’s Edge in Data and AI Security

Sentra stands out for its practical approach to solving urgent security problems, including:

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• Agentless, multi-cloud coverage: Sentra identifies and classifies sensitive data and AI agents across cloud, SaaS, and on-premises environments without any agents or hidden gaps.
• Integrated DSPM + DDR: We go further than monitoring posture by automatically investigating incidents and responding, so security teams can act quickly on why DSPM+DDR matters.
• AI-driven advancements: Features like domain-specific AI Classifiers for Unstructure advanced AI classification leveraging SLMs, Data Security for AI Agents and Microsoft M365 Copilot help customers stay in control as they adopt new technologies Sentra’s AI-powered innovation.

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With new attack surfaces popping up all the time, from prompt injection to autonomous agent drift, Sentra’s architecture is built to handle the world of AI.

A Platform Approach That Outpaces the Competition

There are plenty of startups aiming to tackle AI, cloud, and data security challenges. Companies like 7AI, Reco, Exaforce, and Noma Security have been in the news for their funding rounds and targeted solutions. Still, very few offer the kind of unified coverage that sets Sentra apart.

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Most competitors stick to either monitoring SaaS agents or reducing SOC alerts. Sentra does more by providing both agentless multi-cloud DSPM and built-in DDR. This gives organizations visibility, context, and the power to act in one platform. With features like Data Security for AI Agents, Sentra helps enterprises go beyond managing alerts by automating meaningful steps to defend sensitive data everywhere.

Thanks to Our Community and What’s Next

This honor belongs first and foremost to our community: customers breaking new ground in data security, partners building solutions alongside us, and a team with a clear goal to lead the industry.

If you haven’t tried Sentra yet, now’s a great time to see what we can do for your cloud and AI data security program. Find out why we’re at the forefront: schedule a personalized demo or read CRN’s full 2025 list for more insight.

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Conclusion

Being named one of CRN’s hottest cybersecurity startups isn’t just a milestone. It pushes us forward toward our vision - data security that truly enables innovation. The market is changing fast, but Sentra’s focus on meaningful security results hasn't wavered.

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Thank you to our customers, partners, investors, and team for your ongoing trust and teamwork. As AI and cloud technology shape the future, Sentra is ready to help organizations move confidently, securely, and quickly.

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Generative AI isn’t just transforming products and processes - it’s expanding the entire enterprise risk surface. As C-suite executives and security leaders rush to unlock GenAI’s competitive advantages, a hard truth is clear: effective AI governance depends on solid, end-to-end data governance.

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Sensitive data is increasingly used for model training and autonomous agents. If organizations fail to discover, classify, and secure these resources early, they risk privacy breaches, regulatory violations, and reputational damage. To make GenAI safe, compliant, and trustworthy from the start, data governance for generative AI needs to be a top boardroom priority.

Why Data Governance is the Cornerstone of GenAI Trustworthiness and Safety

The opportunities and risks of generative AI depend not only on algorithms, but also on the quality, security, and history of the underlying data. AWS reports that 39% of Chief Data Officers see data cleaning, integration, and storage as the main barriers to GenAI adoption, and 49% of enterprises make data quality improvement a core focus for successful AI projects (AWS Enterprise Strategy - Data Governance). Without strong data governance, sensitive information can end up in training sets, leading to unintentional leaks or model behaviors that break privacy and compliance.

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Regulatory requirements, such as the Generative AI Copyright Disclosure Act, are evolving fast, raising the pressure to document data lineage and make sure unauthorized or non-compliant datasets stay out. In the world of GenAI, governance goes far beyond compliance checklists. It’s essential for building AI that is safe, auditable, and trusted by both regulators and customers.

New Attack Surfaces: Risks From Unsecured Data and Shadow AI Agents

GenAI adoption increases risk. Today, 79% of organizations have already piloted or deployed agentic AI, with many using LLM-powered agents to automate key workflows (Wikipedia - Agentic AI). But if these agents, sometimes functioning as "shadow AI" outside official oversight, access sensitive or unclassified data, the fallout can be severe.

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In 2024, over 30% of AI data breaches involve insider threats or accidental disclosure, according to Quinnox Data Governance for AI. Autonomous agents can mistakenly reveal trade secrets, financial records, or customer data, damaging brand trust. The risk multiplies rapidly if sensitive data isn’t properly governed before flowing into GenAI tools. To stop these new threats, organizations need up-to-the-minute insight and control over both data and the agents using it.

Frameworks and Best Practices for Data Governance in GenAI

Leading organizations now follow data governance frameworks that match changing regulations and GenAI's technical complexity. Standards like NIST AI Risk Management Framework (AI RMF) and ISO/IEC 42001:2023 are setting the benchmarks for building auditable, resilient AI programs (Data and AI Governance - Frameworks & Best Practices).

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Some of the most effective practices:

• Managing metadata and tracking full data lineage
• Using data access policies based on role and context
• Automating compliance with new AI laws
• Monitoring data integrity and checking for bias

A strong data governance program for generative AI focuses on ongoing data discovery, classification, and policy enforcement - before data or agents meet any AI models. This approach helps lower risk and gives GenAI efforts a solid base of trust.

Sentra’s Approach: Proactive Pre-Integration Discovery and Continuous Enforcement

Many tools only secure data after it’s already being used with GenAI applications. This reactive strategy leaves openings for risk. Sentra takes a different path, letting organizations discover, classify, and protect sensitive data sources before they interact with language models or agentic AI.

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By using agentless, API-based discovery and classification across multi-cloud and SaaS environments, Sentra delivers immediate visibility and context-aware risk scoring for all enterprise data assets. With automated policies, businesses can mask, encrypt, or restrict data access depending on sensitivity, business requirements, or audit needs. Live Continuous monitoring tracks which AI agents are accessing data, making granular controls and fast intervention possible. These processes help stop shadow AI, keep unauthorized data out of LLM training, and maintain compliance as rules and business needs shift.

Guardrails for Responsible AI Growth Across the Enterprise

The future of GenAI depends on how well businesses can innovate while keeping security and compliance intact. As AI regulations become stricter and adoption speeds up, Sentra’s ability to provide ongoing, automated discovery and enforcement at scale is critical. Further reading: AI Automation & Data Security: What You Need To Know.

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With Sentra, organizations can:

• Stop unapproved or unchecked data from being used in model training
• Identify shadow AI agents or risky automated actions as they happen
• Support audits with complete data classification
• Meet NIST, ISO, and new global standards with ease

Sentra gives CISOs, CDOs, and executives a proactive, scalable way to adopt GenAI safely, protecting the business before any model training even begins.

AI Governance Starts with Data Governance

AI governance for generative AI starts, and is won or lost, at the data layer. If organizations don’t find, classify, and secure sensitive data first, every other security measure remains reactive and ineffective. As generative AI, agent automation, and regulatory demands rise, a unified data governance strategy isn’t just good practice, it’s an urgent priority. Sentra gives security and business teams real control, making sure GenAI is secure, compliant, and trusted.

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By 2026, American data privacy will look very different as a wave of new state laws redefines what it means to protect sensitive information. Organizations face a regulatory maze: more than 20 states will soon require not only “reasonable security” but also Data Protection Impact Assessments (DPIAs), explicit limits on data collection, and, in some cases, detailed data inventories. These requirements are quickly becoming standard, and ignoring them simply isn’t an option. The risk of penalties and enforcement actions is climbing fast.

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But through all these changes, one major question remains: How can any organization comply if it doesn’t even know where its most sensitive data is? Data Security Posture Management (DSPM) has become the solution, making data visibility and automation central for meeting ongoing compliance needs.

Mapping the New Wave of State Privacy Mandates

Several state privacy laws going into effect in 2025 and 2026 are raising the stakes for compliance. Kentucky, Indiana, and Rhode Island’s new laws, effective January 1, 2026, require both security measures and DPIAs for handling high-risk or sensitive data. Minnesota’s law stands out even more: it moves past earlier vague “reasonable” security language and mandates comprehensive data inventories.

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Other key states include Minnesota, which explicitly requires data inventories, Maryland with strict data minimization rules, and Tennessee, which gives organizations an affirmative defense if they’ve adopted a NIST-aligned privacy program. These requirements mean organizations now need to track what data they collect, know exactly where it’s stored, and show evidence of compliance when asked. If your organization operates in more than one state, keeping up with this web of laws will soon become impossible without dedicated solutions (US consumer privacy laws 2025 update).

Why Data Visibility is Now Foundational to Compliance

To meet DPIA, minimization, and security safeguard rules, you need full visibility into where sensitive or regulated data lives - and how it moves across your environment. Recent privacy laws are moving closer to GDPR-like standards, with DPIAs required not only for biometric data but also for broad categories like targeted advertising and profiling. Minnesota leads with its clear requirement for full data inventories, setting the standard that you can’t prove compliance unless you understand your data (US cybersecurity and data privacy review and outlook 2025).

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This shift puts DSPM front and center: you now need ongoing discovery and classification of your entire sensitive data footprint. Without a strong data foundation, organizations will find it hard to complete DPIAs, handle audits, or defend themselves in investigations.

Automation: The Only Viable Path for Assessment and Audit Readiness

State privacy rules are getting more complicated, and many enforcement authorities are shortening or removing 'right-to-cure' periods. That means manual compliance simply won’t keep up. Automation is now the only way to manage compliance as regulations tighten (5 trends to watch: 2025 US data privacy & cybersecurity).

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With DSPM and automation, organizations get ongoing discovery, real-time data classification, and instant evidence collection - all required for fast DPIAs and responsive audits. For companies facing regulators or preparing for multi-state oversight, this means you already have the proof and documentation you need. Relying on spreadsheets or one-time assessments at this point only increases your risk.

Sentra: Your Strategic Bridge to Privacy Law Compliance

Sentra’s DSPM platform is built to tackle these expanding privacy law requirements. The agentless platform covers AWS, Azure, GCP, SaaS, and hybrid environments, removing both visibility gaps and the hassle found in older solutions (Sentra: DSPM for compliance use cases).

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With continuous, automated discovery and data classification, you always know exactly where your sensitive data is, how it moves, and how it’s being protected. Sentra’s integrated Data Detection & Response (DDR) catches and fixes risks or policy violations early, closing gaps before regulators - or attackers - can take advantage (Sensitive data exposure insight). Combined with clear reporting and on-demand audit documentation, Sentra helps you meet new state privacy laws and stay audit-ready, even as your business or data needs change.

Conclusion

The arrival of new state privacy laws in 2025 and 2026 is changing how organizations must handle sensitive data. Security safeguards, DPIAs, minimization, and full inventories are now required - not just nice-to-have.

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DSPM is now a compliance must-have. Without complete data visibility and automation, following the web of state rules isn’t difficult - it’s impossible. Sentra’s agentless, multi-cloud platform keeps your organization continuously informed, giving compliance, security, and privacy teams the control they need to keep up with new regulations.

Want to see how your organization stacks up for 2026 laws? Book a DSPM Compliance Readiness Assessment or check out Sentra’s automated DPIA tools today.

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