Data: The Unifying Force Behind Disparate GRC Functions

“If you don’t know where your sensitive data is, how can you protect it?”

‍

It’s a simple question, but for many security and compliance teams, it’s nearly impossible to answer. When a Fortune 500 company recently paid millions in fines due to improperly stored customer data on an unmanaged cloud bucket, the real failure wasn’t just a misconfiguration. It was a lack of visibility.

‍

Some in the industry are starting to refer to this challenge as "data blindness".

What Is Data Blindness?

Data Blindness refers to an organization’s inability to fully see, classify, and understand the sensitive data spread across its cloud, SaaS, and hybrid environments.

It’s not just another security buzzword. It’s the modern evolution of a very real problem: traditional data protection methods weren’t built for the dynamic, decentralized, and multi-cloud world we now operate in. Legacy DLP tools or one-time audits simply can’t keep up.

Unlike general data security issues, Data Blindness speaks to a specific kind of operational gap: you can’t protect what you can’t see, and most teams today are flying partially blind.

Why Data Blindness Is Getting Worse

What used to be a manageable gap in visibility has now escalated into a full-scale operational risk. As organizations accelerate cloud adoption and embrace SaaS-first architectures, the complexity of managing sensitive data has exploded. Information no longer lives in a few centralized systems, it’s scattered across AWS, Azure, and GCP instances, and a growing stack of SaaS tools, each with its own storage model, access controls, and risk profile.

At the same time, shadow data is proliferating. Sensitive information ends up in collaboration platforms, forgotten test environments, and unsanctioned apps - places that rarely make it into formal security inventories. And with the rise of generative AI tools, a new wave of unstructured content is being created and shared at scale, often without proper visibility or retention controls in place.

‍

To make matters worse, many organizations are still operating with outdated identity and access frameworks. Stale permissions and misconfigured policies allow unnecessary access to critical data, dramatically increasing the potential impact of both internal mistakes and external breaches.

In short, the cloud hasn’t just moved the data, it’s multiplied it, fragmented it, and made it harder than ever to track. Without continuous, intelligent visibility, data blindness becomes the default.

The Hidden Risks of Operating Blind

When teams don’t have visibility into where sensitive data lives or how it moves, the consequences stack up quickly:

‍

• Compliance gaps: Regulations like GDPR, HIPAA, and PCI-DSS demand accurate data inventories, privacy adherence, and prompt response to DSARs. Without visibility, you risk fines and legal exposure.
  
  
• Breach potential: Blind spots become attack vectors. Misplaced data, overexposed buckets, or forgotten environments are easy targets.
  
  
• Wasted resources: Scanning everything (just in case) is expensive. Without prioritization, teams waste cycles on low-risk data.
  
  
• Trust erosion: Customers expect you to know where their data is and how it’s protected. Data blindness isn’t a good look.
  
  

Do You Have Data Blindness? Here Are the Signs

• Your security team can’t confidently answer, “Where is our most sensitive data and who has access to it?”
  
  
• Data inventories are outdated, or built on manual tagging and spreadsheets.
  
  
• You’re still relying on legacy DLP tools with poor context and high false positives.
  
  
• Incident response is slow because it’s unclear what data was touched or how sensitive it was.

‍

Sound familiar? You’re not alone.

Breaking Free from Data Blindness

Solving data blindness starts with visibility, but real progress comes from turning that visibility into action. Modern organizations need more than one-off audits or static reports. They need continuous data discovery that scans cloud, SaaS, and on-prem environments in real time, keeping up with the constant movement of data.

But discovery alone isn’t enough. Classification must go beyond content analysis, it needs to be context-aware, taking into account where the data lives, who has access to it, how it’s used, and why it matters to the business. Visibility must extend to both structured and unstructured data, since sensitive information often hides in documents, PDFs, chat logs, and spreadsheets. And finally, insights need to be integrated into existing security and compliance workflows. Detection without action is just noise.

How Sentra Solves Data Blindness

At Sentra, we give security and privacy teams the visibility and context they need to take control of their data - without disrupting operations or moving it out of place. Our cloud-native DSPM (Data Security Posture Management) platform scans and classifies data in-place across cloud, SaaS, and on-prem environments, with no agents or data removal required.

‍

Sentra uses AI-powered, context-rich classification to achieve over 95% accuracy, helping teams identify truly sensitive data and prioritize what matters most. We provide full coverage of structured and unstructured sources, along with real-time insights into risk exposure, access patterns, and regulatory posture, all with a cost-efficient scanning model that avoids unnecessary compute usage.

One customer reduced their shadow data footprint by 30% in just a few weeks, eliminating blind spots that their legacy tools had missed for years. That’s the power of visibility, backed by context, at scale.

The Bottom Line: Awareness Is Step One

Data Blindness is real, but it’s also solvable. The first step is acknowledging the problem. The next is choosing a solution that brings your data out of the dark, without slowing down your teams or compromising security.

‍

If you’re ready to assess your current exposure or just want to see what’s possible with modern data security, you can take a free data blindness assessment, or talk to our experts to get started.

‍

‍

<blogcta-big>

‍

When we started Sentra three years ago, we had a hypothesis: organizations were drowning in data they couldn't see, classify, or protect. What we didn't anticipate was how brutally honest our customers would be about what actually works, and what doesn't.

‍

This week, Gartner named Sentra a "Customer's Choice" in their Peer Insights Voice of the Customer report for Data Security Posture Management. The recognition is based on over 650 verified customer reviews, giving us a 4.9/5 rating with 98% willing to recommend us.

The Accuracy Obsession Was Right

The most consistent theme across hundreds of reviews? Accuracy matters more than anything else.

‍

"97.4% of Sentra's alerts in our testing were accurate! By far the highest percentage of any of the DSPM platforms that we tested."

‍

‍"Sentra accurately identified 99% of PII and PCI in our cloud environments with minimal false positives during the POC."

‍

But customers don't just want data discovery—they want trustworthy data discovery. When your DSPM tool incorrectly flags non-sensitive data as critical, teams waste time investigating false leads. When it misses actual sensitive data, you face compliance gaps and real risk. The reviews validate what we suspected: if security teams can't trust your classifications, the tool becomes shelf-ware. Precision isn't a nice-to-have—it's everything.

How Sentra Delivers Time-to-Value

Another revelation: customers don't just want fast deployment, they want fast insights.

"Within less than a week we were getting results, seeing where our sensitive data had been moved to."

"We were able to start seeing actionable insights within hours."

‍

I used to think "time-to-value" was a marketing term. But when you're a CISO trying to demonstrate ROI to your board, or a compliance officer facing an audit deadline, every day matters. Speed isn’t a luxury in security, it’s a necessity. Data breaches don't wait for your security tools to finish their months-long deployment cycles. Compliance deadlines don't care about your proof-of-concept timeline. Security teams need to move at the speed of business risk.

The Honesty That Stings (And Helps)

But here's what really struck me: our customers were refreshingly honest about our shortcomings.

‍

"The chatbot is more annoying than helpful."

‍

‍"Currently there is no SaaS support for something like Salesforce."

‍

"It's a startup so it has all the advantages and disadvantages that those come with."

‍

As a founder, reading these critiques was... uncomfortable. But it's also incredibly valuable. Our customers aren't just users, they're partners in our product evolution. They're telling us exactly where to invest our engineering resources.

The Salesforce integration requests, for instance, showed up in nearly every "dislike" section. Message received. We're shipping SaaS connectors specifically because it’s a top priority for our customers.

What Gartner Customer Choice Trends Reveal About the DSPM Market

Analyzing 650 reviews across 9 vendors revealed something fascinating about our market's maturity. Customers aren't just comparing features, they're comparing outcomes.

‍

The traditional data security playbook focused on coverage: "How many data sources can you scan?" But customers are asking different questions:

‍

• How accurate are your findings?
• How quickly can I act on your insights?
• How much manual work does this actually eliminate?

This shift from inputs to outcomes suggests the DSPM market is maturing rapidly. 

The Gartner Voice of the Customer Validated

Perhaps the most meaningful insight came from what customers didn't say. I expected more complaints about deployment complexity, integration challenges, or learning curves. Instead, review after review mentioned how quickly teams became productive with Sentra.

‍

"It was also the fastest set up."

"Quick setup and responsive support."

"The platform is intuitive and offers immediate insights."

‍

This tells me we're solving a real problem in a way that feels natural to security teams. The best products don't just work, they feel inevitable once you use them.

The Road Ahead: Learning from Gartner Choice Recognition

These reviews crystallized our 2025 roadmap priorities:

‍

1. SaaS-First Expansion: Every customer asked for broader SaaS coverage. We're expanding beyond IaaS to support the applications where your most sensitive data actually lives. Our mission is to secure data everywhere.

‍

2. AI Enhancement: Our classification engine is industry-leading, but customers want more. We're building contextual AI that doesn't just find data, it understands data relationships and business impact.

‍

‍3. Remediation Automation: Customers love our visibility but want more automated remediation. We're moving beyond recommendations to actual risk mitigation.

A Personal Thank You

To the customers who contributed to our Sentra Gartner Peer Insights success: thank you. Building a startup is often a lonely journey of best guesses and gut instincts. Your feedback is the compass that keeps us pointed toward solving real problems.

‍

To the security professionals reading this: your honest feedback (both praise and criticism) makes our products better. If you're using Sentra, please keep telling us what's working and what isn't. If you're not, I'd love to show you what earned us Customer Choice 2025 recognition and why 98% of our customers recommend us.

‍

The data security landscape is evolving rapidly. But with customers as partners and recognition like Gartner Peer Insights Customer Choice 2025, I'm confident we're building tools that don't just keep up with threats, they help organizations stay ahead of them.

<blogcta-big>

‍

In today’s fast-moving digital landscape, enterprise AI adoption presents a fascinating paradox for leaders: AI isn’t just a tool for innovation; it’s also a gateway to new security challenges. Organizations are walking a tightrope: Adopt AI to remain competitive, or hold back to protect sensitive data.
With nearly two-thirds of security leaders even considering a ban on AI-generated code due to potential security concerns, it’s clear that this tension is creating real barriers to AI adoption.

‍

A data-first security approach provides solid guarantees for enterprises to innovate with AI safely. Since AI thrives on data - absorbing it, transforming it, and creating new insights - the key is to secure the data at its very source.

Let’s explore how data security for AI can build robust guardrails throughout the AI lifecycle, allowing enterprises to pursue AI innovation confidently.

Data Security Concerns with AI

Every AI system is only as strong as its weakest data link. Modern AI models rely on enormous data sets for both training and inference, expanding the attack surface and creating new vulnerabilities. Without tight data governance, even the most advanced AI models can become entry points for cyber threats.

How Does AI Store And Process Data?

The AI lifecycle includes multiple steps, each introducing unique vulnerabilities. Let’s consider the three main high-level stages in the AI lifecycle:

‍

• Training: AI models extract and learn patterns from data, sometimes memorizing sensitive information that could later be exposed through various attack vectors.
  ‍
• Storage: Security gaps can appear in model weights, vector databases, and document repositories containing valuable enterprise data.
  ‍
• Inference: This prediction phase introduces significant leakage risks, particularly with retrieval-augmented generation (RAG) systems that dynamically access external data sources.

Data is everywhere in AI. And if sensitive data is accessible at any point in the AI lifecycle, ensuring complete data protection becomes significantly harder.

AI Adoption Challenges

Reactive measures just won’t cut it in the rapidly evolving world of AI. Proactive security is now a must. Here’s why:

‍

1. AI systems evolve faster than traditional security models can adapt.

New AI models (like DeepSeek and Qwen) are popping up constantly, each introducing novel attack surfaces and vulnerabilities that can change with every model update..

Legacy security approaches that merely react to known threats simply can't keep pace, as AI demands forward-thinking safeguards.
‍

2. Reactive approaches usually try to remediate at the last second.

Reactive approaches usually rely on low-latency inline AI output monitoring, which is the last step in a chain of failures that lead to data loss and exfiltration, and the most challenging position to prevent data-related incidents. 

Instead, data security posture management (DSPM) for AI addresses the issue at its source, mitigating and remediating sensitive data exposure and enforcing a least-privilege, multi-layered approach from the outset.
‍

3. AI adoption is highly interoperable, expanding risk surfaces.

Most enterprises now integrate multiple AI models, frameworks, and environments (on-premise AI platforms, cloud services, external APIs) into their operations. These AI systems dynamically ingest and generate data across organizational boundaries, challenging consistent security enforcement without a unified approach.
‍

Traditional security strategies, which only respond to known threats, can’t keep pace. Instead, a proactive, data-first security strategy is essential. By protecting information before it reaches AI systems, organizations can ensure AI applications process only properly secured data throughout the entire lifecycle and prevent data leaks before they materialize into costly breaches.

‍

Of course, you should not stop there: You should also extend the data-first security layer to support multiple AI-specific controls (e.g., model security, endpoint threat detection, access governance).

What Are the Security Concerns with AI for Enterprises?

Unlike conventional software, AI systems continuously learn, adapt, and generate outputs, which means new security risks emerge at every stage of AI adoption. Without strong security controls, AI can expose sensitive data, be manipulated by attackers, or violate compliance regulations.
‍

For organizations pursuing AI for organization-wide transformation, understanding AI-specific risks is essential:
‍

• Data loss and exfiltration: AI systems essentially share information contained in their training data and RAG knowledge sources and can act as a “tunnel” through existing data access governance (DAG) controls, with the ability to find and output sensitive data that the user is not authorized to access.
  In addition, Sentra’s rich best-of-breed sensitive data detection and classification empower AI to perform DLP (data loss prevention) measures autonomously by using sensitivity labels.

• Compliance & privacy risks: AI systems that process regulated information without appropriate controls create substantial regulatory exposure. This is particularly true in heavily regulated sectors like healthcare and financial services, where penalties for AI-related data breaches can reach millions of dollars.
  ‍
• Data poisoning: Attackers can subtly manipulate training and RAG data to compromise AI model performance or introduce hidden backdoors, gradually eroding system reliability and integrity.
  ‍
• Model theft: Proprietary AI models represent significant intellectual property investments. Inadequate security can leave such valuable assets vulnerable to extraction, potentially erasing years of AI investment advantage.
  ‍‍
• Adversarial attacks: These increasingly prevalent threats involve strategic manipulations of AI model inputs designed to hijack predictions or extract confidential information. Adequate machine learning endpoint security has become non-negotiable.

All these risks stem from a common denominator: a weak data security foundation allowing for unsecured, exposed, or manipulated data.
‍

The solution? A strong data security posture management (DSPM) coupled with comprehensive visibility into the AI assets in the system and the data they can access and expose. This will ensure AI models only train on and access trusted data, interact with authorized users and safe inputs, and prevent unintended exposure.

AI Endpoint Security Risks

Organizations seeking to balance innovation with security must implement strategic approaches that protect data throughout the AI lifecycle without impeding development.

Choosing an AI security solution: ‘DSPM for AI’ vs. AI-SPM

When evaluating security solutions for AI implementation, organizations typically consider two primary approaches:
‍

• Data security posture management (DSPM) for AI implements data-related AI security features while extending capabilities to encompass broader data governance requirements. ‘DSPM for AI’ focuses on securing data before it enters any AI pipeline and the identities that are exposed to it through Data Access Governance. It also evaluates the security posture of the AI in terms of data (e.g., a CoPilot with access to sensitive data, that has public access enabled).
  ‍
• AI security posture management (AI-SPM) focuses on securing the entire AI pipeline, encompassing models and MLOps workflows. AI-SPM features include AI training infrastructure posture (e.g., the configuration of the machine on which training runs) and AI endpoint security.

While both have merits, ‘DSPM for AI’ offers a more focused safety net earlier in the failure chain by protecting the very foundation on which AI operatesーdata. Its key functionalities include data discovery and classification, data access governance, real-time leakage and anomalous “data behavior” detection, and policy enforcement across both AI and non-AI environments.

Best Practices for AI Security Across Environments

AI security frameworks must protect various deployment environments—on-premise, cloud-based, and third-party AI services. Each environment presents unique security challenges that require specialized controls.
‍

On-Premise AI Security
‍

On-premise AI platforms handle proprietary or regulated data, making them attractive for sensitive use cases. However, they require stronger internal security measures to prevent insider threats and unauthorized access to model weights or training data that could expose business-critical information.
‍

Best practices:
‍

• Encrypt AI data at multiple stages—training data, model weights, and inference data. This prevents exposure even if storage is compromised.
  ‍
• Set up role-based access control (RBAC) to ensure only authorized parties can gain access to or modify AI models.
  ‍
• Perform AI model integrity checks to detect any unauthorized modifications to training data or model parameters (protecting against data poisoning).

Cloud-Based AI Security
‍

While home-grown cloud AI services offer enhanced abilities to leverage proprietary data, they also expand the threat landscape. Since AI services interact with multiple data sources and often rely on external integrations, they can lead to risks such as unauthorized access, API vulnerabilities, and potential data leakage.  
‍

Best practices:
‍

• Follow a zero-trust security model that enforces continuous authentication for AI interactions, ensuring only verified entities can query or fine-tune models.
  ‍
• Monitor for suspicious activity via audit logs and endpoint threat detection to prevent data exfiltration attempts.
  ‍
• Establish robust data access governance (DAG) to track which users, applications, and AI models access what data.

Third-Party AI & API Security
‍

Third-party AI models (like OpenAI's GPT, DeepSeek, or Anthropic's Claude) offer quick wins for various use cases. Unfortunately, they also introduce shadow AI and supply chain risks that must be managed due to a lack of visibility.
‍

Best practices:
‍

• Restrict sensitive data input to third-party AI models using automated data classification tools.
  ‍
• Monitor external AI API interactions to detect if proprietary data is being unintentionally shared.
  ‍
• Implement AI-specific DSPM controls to ensure that third-party AI integrations comply with enterprise security policies.

Common AI implementation challenges arise when organizations attempt to maintain consistent security standards across these diverse environments. For enterprises navigating a complex AI adoption, a cloud-native DSPM solution with AI security controls offers a solid AI security strategy.

‍

The Sentra platform is adaptable, consistent across environments, and compliant with frameworks like GDPR, CCPA, and industry-specific regulations.

Use Case: Securing GenAI at Scale with Sentra

Consider a marketing platform using generative AI to create branded content for multiple enterprise clients—a common scenario facing organizations today.

‍

Challenges:
‍

• AI models processing proprietary brand data require robust enterprise data protection.
• Prompt injections could potentially leak confidential company messaging.
• Scalable security that doesn't impede creative workflows is a must. 

Sentra’s data-first security approach tackles these issues head-on via:
‍

• Data discovery & classification: Specialized AI models identify and safeguard sensitive information.

• Data access governance (DAG): The platform tracks who accesses training and RAG data, and when, establishing accountability and controlling permissions at a granular level.  In addition, access to the AI agent (and its underlying information) is controlled and minimized.
  ‍
• Real-time leakage detection: Sentra’s best-of-breed data labeling engine feeds internal DLP mechanisms that are part of the AI agents (as well as external 3rd-party DLP and DDR tools).  In addition, Sentra monitors the interaction between the users and the AI agent, allowing for the detection of sensitive outputs, malicious inputs, or anomalous behavior.
  ‍
• Scalable endpoint threat detection: The solution protects API interactions from adversarial attacks, securing both proprietary and third-party AI services.
  ‍
• Automated security alerts: Sentra integrates with ServiceNow and Jira for rapid incident response, streamlining security operations.

The outcome: Sentra provides a scalable DSPM solution for AI that secures enterprise data while enabling AI-powered innovation, helping organizations address the complex challenges of enterprise AI adoption.

Takeaways

AI security starts at the data layer - without securing enterprise data, even the most sophisticated AI implementations remain vulnerable to attacks and data exposure. As organizations develop their data security strategies for AI, prioritizing data observability, governance, and protection creates the foundation for responsible innovation.

Sentra's DSPM provides cutting-edge AI security solutions at the scale required for enterprise adoption, helping organizations implement AI security best practices while maintaining compliance with evolving regulations.
‍

Learn more about how Sentra has built a data security platform designed for the AI era.

‍

<blogcta-big>