Sentra Is One of the Hottest Cybersecurity Startups

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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Cloud adoption and the explosion of data have boosted business agility, but they’ve also created new headaches for security teams. As companies move sensitive information into multi-cloud and hybrid environments, old security models start to break down. Shuffling data for scanning and classification adds risk, piles on regulatory complexity, and drives up operational costs.

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Zero Data Movement (ZDM) offers a new architectural approach, reshaping how advanced Data Security Posture Management (DSPM) platforms provide visibility, protection, and compliance. This post breaks down what makes ZDM unique, why it matters for security-focused enterprises, and how Sentra provides an innovative agentless and scalable design that is genuinely a zero data movement DSPM .

Defining Zero Data Movement Architecture

Zero Data Movement (ZDM) sets a new standard in data security. The premise is straightforward: sensitive data should stay in its original environment for security analysis, monitoring, and enforcement. Older models require copying, exporting, or centralizing data to scan it, while ZDM ensures that all security actions happen directly where data resides.

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ZDM removes egress risk -shrinking the attack surface and reducing regulatory issues. For organizations juggling large cloud deployments and tight data residency rules, ZDM isn’t just an improvement - it's essential. Groups like the Cloud Security Alliance and new privacy regulations are moving the industry toward designs that build in privacy and non-stop protection.

Risks of Data Movement: Compliance, Cost, and Egress Exposure

Every time data is copied, exported, or streamed out of its native environment, new risks arise. Data movement creates challenges such as:

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• Egress risk: Data at rest or in transit outside its original environment  increases risk of breach, especially as those environments may be less secure.
• Compliance and regulatory exposure: Moving data across borders or different clouds can break geo-fencing and privacy controls, leading to potential violations and steep fines.
• Loss of context and control: Scattered data makes it harder to monitor everything, leaving gaps in visibility.
• Rising total cost of ownership (TCO): Scanning and classification can incur heavy cloud compute costs - so efficiency matters.  Exporting or storing data, especially shadow data, drives up storage, egress, and compliance costs as well.

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As more businesses rely on data, moving it unnecessarily only increases the risk - especially with fast-changing cloud regulations.

Legacy and Competitor Gaps: Why Data Movement Still Happens

Not every security vendor practices true zero data movement, and the differences are notable. Products from Cyera, Securiti, or older platforms still require temporary data exporting or duplication for analysis. This might offer a quick setup, but it exposes users to egress risks, insider threats, and compliance gaps - problems that are worse in regulated fields.

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Competitors like Cyera often rely on shortcuts that fall short of ZDM’s requirements. Securiti and similar providers depend on connectors, API snapshots, or central data lakes, each adding potential risks and spreading data further than necessary. With ZDM, security operations like monitoring and classification happen entirely locally, removing the need to trust external storage or aggregation. For more detail on how data movement drives up risk.

The Business Value of Zero Data Movement DSPM

Zero data movement DSPM changes the equation for businesses:

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• Designed for compliance: Data remains within controlled environments, shrinking audit requirements and reducing breach likelihood.
• Lower TCO and better efficiency: Eliminates hidden expenses from extra storage, duplicate assets, and exporting to external platforms.
• Regulatory clarity and privacy: Supports data sovereignty, cross-border rules, and new zero trust frameworks with an egress-free approach.

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Sentra’s agentless, cloud-native DSPM provides these benefits by ensuring sensitive data is never moved or copied. And Sentra delivers these benefits at scale - across multi-petabyte enterprise environments - without the performance and cost tradeoffs others suffer from. Real scenarios show the results: financial firms keep audit trails without data ever leaving allowed regions. Healthcare providers safeguard PHI at its source. Global SaaS companies secure customer data at scale, cost-effectively while meeting regional rules.

Future-Proofing Data Security: ZDM as the New Standard

With data volumes expected to hit 181 zettabytes in 2025, older protection methods that rely on moving data can’t keep up. Zero data movement architecture meets today's security demands and supports zero trust, metadata-driven access, and privacy-first strategies for the future.

Companies wanting to avoid dead ends should pick solutions that offer unified discovery, classification and policy enforcement without egress risk. Sentra’s ZDM architecture makes this possible, allowing organizations to analyze and protect information where it lives, at cloud speed and scale.

Conclusion

Zero Data Movement is more than a technical detail - it's a new architectural standard for any organization serious about risk control, compliance, and efficiency. As data grows and regulations become stricter, the old habits of moving, copying, or centralizing sensitive data will no longer suffice.

Sentra stands out by delivering a zero data movement DSPMplatform that's agentless, real-time, and truly multicloud. For security leaders determined to cut egress risk, lower compliance spending, and get ahead in privacy, ZDM is the clear path forward.

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