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Cloud Security Strategy: Key Elements, Principles, and Challenges

January 22, 2024
6
 Min Read
Data Security

What is a Cloud Security Strategy?

During the initial phases of digital transformation, organizations may view cloud services as an extension of their traditional data centers. But to fully harness cloud security, there must be progression beyond this view.

A cloud security strategy is an extensive framework that outlines how an organization manages its dynamic, software-defined security ecosystem and protects its cloud-based assets. Security, in its essence, is about managing risk – addressing the probability and impact of attacks instead of eliminating them outright. This reality essentially positions security as a continuous endeavor rather than being a finite problem with a singular solution.

Cloud security strategy advocates for:

  • Ensuring the cloud framework’s integrity: Involves implementing security controls as a foundational part of cloud service planning and operational processes. The aim is to ensure that security measures are a seamless part of the cloud environment, guarding every resource.

  • Harnessing cloud capabilities for defense: Employing the cloud as a force multiplier to bolster overall security posture. This shift in strategy leverages the cloud's agility and advanced capabilities to enhance security mechanisms, particularly those natively integrated into the cloud infrastructure.

Why is a Cloud Security Strategy Important?

Some organizations make the mistake of miscalculating the duality of productivity and security. They often learn the hard way that while innovation drives competitiveness, robust security preserves it. The absence of either can lead to diminished market presence or organizational failure. As such, a balanced focus on both fronts is paramount.

Customers are more likely to do business with organizations that consistently retain the trust to protect proprietary data. When a single instance of a data breach or a security incident that can erode customer trust and damage an organization's reputation, the stakes are naturally high. A cloud security strategy can help organizations address these challenges by providing a framework for managing risk.

A well-crafted cloud security strategy will include the following:

  • Risk assessment to identify and prioritize the organization's key security risks.
  • Set of security controls to mitigate those risks.
  • Process framework for monitoring and improving the security posture of the cloud environment over time.

Key Elements of a Cloud Security Strategy

Tactically, a cloud security strategy empowers organizations to navigate the complexities of shared responsibility models, where the burden of security is divided between the cloud provider and the client.

Key Element Description Objectives Tools/Technologies
Data Protection Safeguarding data from unauthorized access and ensuring its availability, integrity, and confidentiality. - Ensure data privacy and regulatory compliance
- Prevent data breaches
- Data Loss Prevention (DLP)
- Backup and recovery solutions
Infrastructure Protection Securing the underlying cloud infrastructure including servers, storage, and network components. - Protect against vulnerabilities
- Secure the physical and virtual infrastructure
- Network security controls
- Intrusion detection systems
Identity and Access Management (IAM) Managing user identities and governing access to resources based on roles. - Implement least privilege access
- Manage user identities and credentials
- IAM services (e.g., AWS IAM, Azure Active Directory)
- Multi-factor authentication (MFA)
Automation Utilizing technology to automate repetitive security tasks. - Reduce human errors
- Streamline security workflows
- Automation scripts
- Security orchestration, automation, and response (SOAR) systems
Encryption Encoding data to protect it from unauthorized access. - Protect data at rest and in transit
- Ensure data confidentiality
- Encryption protocols (e.g., TLS, SSL)
- Key management services
Detection & Response Identifying potential security threats and responding effectively to mitigate risks. - Detect security incidents in real-time
- Respond to and recover from incidents quickly
- Security information and event management (SIEM)
- Incident response platforms

Key Challenges in Building a Cloud Security Strategy

When organizations shift from on-premises to cloud computing, the biggest stumbling block is their lack of expertise in dealing with a decentralized environment. Some consider agility and performance to be the super-features that led them to adopt the cloud. Anything that impacts the velocity of deployment is met with resistance. As a result, the challenge often lies in finding the sweet spot between achieving efficiency and administering robust security. But in reality, there are several factors that compound the complexity of this challenge.

Lack of Visibility

If your organization lacks insight into its cloud activity, it cannot accurately assess the associated risks. Lack of visibility also introduces multifaceted challenges. Initially, it can be about cataloging active elements in your cloud. Subsequently, it can restrain comprehension of the data, operation, and interconnections of those systems.

Imagine manually checking each cloud service across different HA zones for each provider. You'd be manifesting virtual machines, surveying databases, and tracking user accounts. It's a complex task which can rapidly become unmanageable.

Most major cloud service providers (CSPs) offer monitoring services to streamline this complexity into a more efficient strategy. But even with these tools, you mostly see the numbers—data stores, resources—but not the substance within or their inter-relationship. In reality, a production-grade observability stack depends on a mix of CSP provider tools, third-party services, and architecture blueprints to assess the security landscape.

Human Errors

Surprisingly, the most significant cloud security threat originates from your own IT team's oversights. Gartner estimates that by 2025, a staggering 99% of cloud security failures will be due to human errors.

One contributing factor is the shift to the cloud which demands specialized skills. Seasoned IT professionals who are already well-versed in on-prem security may potentially mishandle cloud platforms. These lapses usually involve issues like misconfigured storage buckets, exposed network ports, or insecure use of accounts. Such mistakes, if unnoticed, offer attackers easy pathways to infiltrate cloud environments.

An organization can likely utilize a mix of service models—Infrastructure as a Service (IaaS) for foundational compute resources, Platform as a Service (PaaS) for middleware orchestration, and Software as a Service (SaaS) for on-demand applications. For each tier, manual security controls might entail crafting bespoke policies for every service. This method provides meticulous oversight, albeit with considerable demands on time and the ever-present risk of human error.

Misconfiguration

OWASP highlights that around 4.51% of applications become susceptible when wrongly configured or deployed. The dynamism of cloud environments, where assets are constantly deployed and updated, exacerbates this risk.

While human errors are more about the skills gap and oversight, the root of misconfiguration often lies in the complexity of an environment, particularly when a deployment doesn’t follow best practices. Cloud setups are intricate, where each change or a newly deployed service can introduce the potential for error. And as cloud offerings evolve, so do the configuration parameters, subsequently increasing the likelihood of oversight.

Some argue that it’s the cloud provider that ensures the security of the cloud. Yet, the shared responsibility model places a significant portion of the configuration management on the user. Besides the lack of clarity, this division often leads to gaps in security postures.

Automated tools can help but have their own limitations. They require precise tuning to recognize the correct configurations for a given context. Without comprehensive visibility and understanding of the environment, these tools tend to miss critical misconfigurations.

Compliance with Regulatory Standards

When your cloud environment sprawls across jurisdictions, adherence to regulatory standards is naturally a complex affair. Each region comes with its mandates, and cloud services must align with them. Data protection laws like GDPR or HIPAA additionally demand strict handling and storage of sensitive information.

The key to compliance in the cloud is a thorough understanding of data residency, how it is protected, and who has access to it. A thorough understanding of the shared responsibility model is also crucial in such settings. While cloud providers ensure their infrastructure meets compliance standards, it's up to organizations to maintain data integrity, secure their applications, and verify third-party services for compliance.

Modern Cloud Security Strategy Principles

Because the cloud-native ecosystem is still an emerging discipline with a high degree of process variations, a successful security strategy calls for a nuanced approach. Implementing security should start with low-friction changes to workflows, the development processes, and the infrastructure that hosts the workload.

Here’s how it can be imagined:

Establishing Comprehensive Visibility

Visibility is the foundational starting point. Total, accessible visibility across the cloud environment helps achieve a deeper understanding of your systems' interactions and behaviors by offering a clear mapping of how data moves and is processed.

Establish a model where teams can achieve up-to-date, easy-to-digest overviews of their cloud assets, understand their configuration, and recognize how data flows between them. Visibility also lays the foundation for traceability and observability. Modern performance analysis stacks leverage the principle of visibility, which eventually leads to traceability—the ability to follow actions through your systems. And then to observability—gaining insight from what your systems output.

Enabling Business Agility

The cloud is known for its agile nature that enables organizations to respond swiftly to market changes, demands, and opportunities. Yet, this very flexibility requires a security framework that is both robust and adaptable. Security measures must protect assets without hindering the speed and flexibility that give cloud-based businesses their edge.

To truly scale and enhance efficiency, your security strategy must blend the organization’s technology, structure, and processes together. This ensures that the security framework is capable of supporting fast-paced development cycles, ensures compliance, and fosters innovation without compromising on protection. In practice, this means integrating security into the development lifecycle from its initial stages, automating security processes where possible, and ensuring that security protocols can accommodate the rapid deployment of services.

Cross-Functional Coordination

A future-focused security strategy acknowledges the need for agility in both action and thought. A crucial aspect of a robust cloud security strategy is avoiding the pitfall where accountability for security risks is mistakenly assigned to security teams rather than to the business owners of the assets. Such misplacement arises from the misconception of security as a static technical hurdle rather than the dynamic risk it can introduce.

Security cannot be a siloed function; instead, every stakeholder has a part to play in securing cloud assets. The success of your security strategy is largely influenced by distinguishing between healthy and unhealthy friction within DevOps and IT workflows. The strategic approach blends security seamlessly into cloud operations, challenging teams to preemptively consider potential threats during design and to rectify vulnerabilities early in the development process. This constructive friction strengthens systems against attacks, much like stress tests to inspect the resilience of a system.

However, the practicality of security in a dynamic cloud setting demands more than stringent measures; it requires smart, adaptive protocols. Excessive safeguards that result in frequent false positives or overcomplicate risk assessments can impact the rapid development cycles characteristic of cloud environments. To counteract this, maintaining the health of relationships within and across teams is essential.

Ongoing and Continuous Improvement

Adopting agile security practices involves shifting from a perfectionist mindset to embracing a baseline of “minimum viable security.” This baseline evolves through continuous incremental improvements, matching the agility of cloud development. In a production-grade environment, this relies on a data-driven approach where user experiences, system performance, and security incidents shape the evolution of the platform.

The commitment to continuous improvement means that no system is ever "finished." Security is seen as an ongoing process, where DevSecOps practices can ensure that every code commit is evaluated against security benchmarks, allowing for immediate correction and learning from any identified issues.

To truly embody continuous improvement though, organizations must foster a culture that encourages experimentation and learning from failures. Blameless postmortems following security incidents, for example, can uncover root causes without fear of retribution, ensuring that each issue is a learning opportunity.

Preventing Security Vulnerabilities Early

A forward-thinking security strategy focuses on preempting risks. The 'shift left' concept evolved to solve this problem by integrating security practices at the very beginning and throughout the application development lifecycle. Practically, this approach embeds security tools and checks into the pipeline where the code is written, tested, and deployed.

Start with outlining a concise strategy document that defines your shift-left approach. It needs a clear vision, designated roles, milestones, and clear metrics. For large corporations, this could be a complex yet indispensable task—requiring thorough mapping of software development across different teams and possibly external vendors.

The aim here is to chart out the lifecycle of software from development to deployment, identifying the people involved, the processes followed, and the technologies used. A successful approach to early vulnerability prevention also includes a comprehensive strategy for supply chain risk management. This involves scrutinizing open-source components for vulnerabilities and establishing a robust process for regularly updating dependencies.

How to Create a Robust Cloud Security Strategy

Before developing a security strategy, assess the inherent risks your organization may be susceptible to. The findings of the risk assessment should be treated as the baseline to develop a security architecture that aligns with your cloud environment's business goals and risk tolerance.

In most cases, a cloud security architecture should include the following combination of technical, administrative and physical controls for comprehensive security:

Access and Authentication Controls

The foundational principle of cloud security is to ensure that only authorized users can access your environment. The emphasis should be on strong, adaptive authentication mechanisms that can respond to varying risk levels.

Build an authentication framework that is non-static. It should scale with risk, assessing context, user behavior, and threat intelligence. This adaptability ensures that security is not a rigid gate but a responsive, intelligent gateway that can be configured to suit the complexity of different cloud environments and sophisticated threat actors.

Actionable Steps

  • Enforce passwordless or multi-factor authentication (MFA) mechanisms to support a dynamic security ethos.
  • Adjust permissions dynamically based on contextual data.
  • Integrate real-time risk assessments that actively shape and direct access control measures.
  • Employ AI mechanisms for behavioral analytics and adaptive challenges.
  • Develop a trust-based security perimeter centered around user identity.

Identify and Classify Sensitive Data

Before classification, locate sensitive cloud data first. Implement enterprise-grade data discovery tools and advanced scanning algorithms that seamlessly integrate with cloud storage services to detect sensitive data points.

Once identified, the data should be tagged with metadata that reflects its sensitivity level; typically by using automated classification frameworks capable of processing large datasets at scale. These systems should be configured to recognize various data privacy regulations (like GDPR, HIPAA, etc.) and proprietary sensitivity levels.

Actionable Steps

  • Establish a data governance framework agile enough to adapt to the cloud's fluid nature.
  • Create an indexed inventory of data assets, which is essential for real-time risk assessment and for implementing fine-grained access controls.
  • Ensure the classification system is backed by policies that dynamically adjust controls based on the data’s changing context and content.

Monitoring and Auditing

Define a monitoring strategy that delivers service visibility across all layers and dimensions. A recommended practice is to balance in-depth telemetry collection with a broad, end-to-end view and east-west monitoring that encompasses all aspects of service health.

Treat each dimension as crucial—depth ensures you're catching the right data, breadth ensures you're seeing the whole picture, and the east-west focus ensures you're always tuned into availability, performance, security, and continuity. This tri-dimensional strategy also allows for continuous compliance checks against industry standards, while helping with automated remediation actions in cases of deviations.

Actionable Steps

  • Implement deep-dive telemetry to gather detailed data on transactions, system performance, and potential security events.
  • Utilize specialized monitoring agents that span across the stack, providing insights into the OS, applications, and services.
  • Ensure full visibility by correlating events across networks, servers, databases, and application performance.
  • Deploy network traffic analysis to track lateral movement within the cloud, which is indicative of potential security threats.

Data Encryption and Tokenization

Construct a comprehensive approach that embeds security within the data itself. This strategy ensures data remains indecipherable and useless to unauthorized entities, both at rest and in transit.

When encrypting data at rest, protocols like AES-256 ensure that should the physical security controls fail, the data remains worthless to unauthorized users. For data in transit, TLS secures the channels over which data travels to prevent interceptions and leaks.

Tokenization takes a different approach by swapping out sensitive data with unique symbols (also known as tokens) to keep the real data secure. Tokens can safely move through systems and networks without revealing what they stand for.

Actionable Steps

  • Embrace strong encryption for data at rest to render it inaccessible to intruders. Implement industry-standard protocols such as AES-256 for storage and database encryption.
  • Mandate TLS protocols to safeguard data in transit, eliminating vulnerabilities during data movement across the cloud ecosystem.
  • Adopt tokenization to substitute sensitive data elements with non-sensitive tokens. This renders the data non-exploitable in its tokenized form.
  • Isolate the tokenization system, maintaining the token mappings in a highly restricted environment detached from the operational cloud services.

Incident Response and Disaster Recovery

Modern disaster recovery (DR) strategies are typically centered around intelligent, automated, and geographically diverse backups. With that in mind, design your infrastructure in a way that anticipates failure, with planning focused on rapid failback.

Planning for the unknown essentially means preparing for all outage permutations. Classify and prepare for the broader impact of outages, which encompass security, connectivity, and access.

Define your recovery time objective (RTO) and recovery point objective (RPO) based on data volatility. For critical, frequently modified data, aim for a low RPO and adjust RTO to the shortest feasible downtime.

Actionable Steps

  • Implement smart backups that are automated, redundant, and cross-zone.
  • Develop incident response protocols specific to the cloud. Keep these dynamic while testing them frequently.
  • Diligently choose between active-active or active-passive configurations to balance expense and complexity.
  • Focus on quick isolation and recovery by using the cloud's flexibility to your advantage.

Conclusion

Organizations must discard the misconception that what worked within the confines of traditional data centers will suffice in the cloud. Sticking to traditional on-premises security solutions and focusing solely on perimeter defense is irrelevant in the cloud arena. The traditional model—where data was a static entity within an organization’s stronghold—is now also obsolete.

Like earlier shifts in computing, the modern IT landscape demands fresh approaches and agile thinking to neutralize cloud-centric threats. The challenge is to reimagine cloud data security from the ground up, shifting focus from infrastructure to the data itself.

Sentra's innovative data-centric approach, which focuses on Data Security Posture Management (DSPM), emphasizes the importance of protecting sensitive data in all its forms. This ensures the security of data whether at rest, in motion, or even during transitions across platforms.

Book a demo to explore how Sentra's solutions can transform your approach to your enterprise's cloud security strategy.

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Daniel is the Data Team Lead at Sentra. He has nearly a decade of experience in engineering, and in the cybersecurity sector. He earned his BSc in Computer Science at NYU.

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Latest Blog Posts

Dean Taler
Dean Taler
September 16, 2025
5
Min Read
Compliance

How to Write an Effective Data Security Policy

How to Write an Effective Data Security Policy

Introduction: Why Writing Good Policies Matters

In modern cloud and AI-driven environments, having security policies in place is no longer enough. The quality of those policies directly shapes your ability to prevent data exposure, reduce noise, and drive meaningful response. A well-written policy helps to enforce real control and provides clarity in how to act. A poorly written one, on the other hand, fuels alert fatigue, confusion, or worse - blind spots.

This article explores how to write effective, low-noise, action-oriented security policies that align with how data is actually used.

What Is a Data Security Policy?

A data security policy is a set of rules that defines how your organization handles sensitive data. It specifies who can access what information, under what conditions, and what happens when those rules are violated. But here's the key difference: a good data security policy isn't just a document that sits in a compliance folder. It's an active control that detects risky behavior and triggers specific responses. While many organizations write policies that sound impressive but create endless alerts, effective policies target real risks and drive meaningful action. The goal isn't to monitor everything, it's to catch the activities that actually matter and respond quickly when they happen.

What Makes a Data Security Policy “Good”?

Before you begin drafting, ask yourself: what problem is this policy solving, and why does it matter? 

A good data security policy isn’t just a technical rule sitting in a console, it’s a sensor for meaningful risk. It should define what activity you want to detect, under what conditions it should trigger, and who or what is in scope, so that it avoids firing on safe, expected scenarios.

Key characteristics of an effective policy:

  • Clear intent: protects against a well-defined risk, not a vague category of threats.
  • Actionable outcome: leads to a specific, repeatable response.
  • Low noise: triggers only on unusual or risky patterns, not normal operations.
  • Context-aware: accounts for business processes and expected data use.

💡 Tip: If you can’t explain in one sentence what you want to detect and what action should happen when it triggers, your policy isn’t ready for production.

Turning Risk Into Actionable Policy

Data security policies should always be grounded in real business risk, not just what’s technically possible to monitor. A strong policy targets scenarios that could genuinely harm the organization if left unchecked.

Questions to ask before creating a policy:

  • What specific behavior poses a risk to our sensitive or regulated data?
  • Who might trigger it, and why? Is it more likely to be malicious, accidental, or operational?
  • What exceptions or edge cases should be allowed without generating noise?
  • What systems will enforce it and who owns the response when it fires?

Instead of vague statements like “No access to PII”, write with precision:


“Block and alert on external sharing of customer PII from corporate cloud storage to any domain not on the approved partner list, unless pre-approved via the security exception process.”

Recommendations:

  • Treat policies like code - start them in monitor-only mode.
  • Test both sides: validate true positives (catching risky activity) and avoid false positives (triggering on normal behavior).

💡 Tip: The best policies are precise enough to detect real risks, but tested enough to avoid drowning teams in noise.

A Good Data Security Policy Should Drive Action

Policies are only valuable if they lead to a decision or action. Without a clear owner or remediation process, alerts quickly become noise. Every policy should generate an alert that leads to accountability.

Questions to ask:

  • Who owns the alert?
  • What should happen when it fires?
  • How quickly should it be resolved?

💡 Tip: If no one is responsible for acting on a policy’s alerts, it’s not a policy — it’s background noise.

Don’t Ignore the Noise

When too many alerts fire, it’s tempting to dismiss them as an annoyance. But noisy policies are often a signal, not a mistake. Sometimes policies are too broad or poorly scoped. Other times, they point to deeper systemic risks, such as overly open sharing practices or misconfigured controls.

Recommendations:

  • Investigate noisy policies before silencing them.
  • Treat excess alerts as a clue to systemic risk.

💡 Tip: A noisy policy may be exposing the exact weakness you most need to fix.

Know When to Adjust or Retire a Policy

Policies must evolve as your organization, tools, and data change. A rule that made sense last year might be irrelevant or counterproductive today.

Recommendations:

  • Continuously align policies with evolving risks.
  • Track key metrics: how often it triggers, severity, and response actions.
  • Optimize response paths so alerts reach the right owners quickly.
  • Schedule quarterly or biannual reviews with both security and business stakeholders.

💡 Tip: The only thing worse than no policy is a stale one that everyone ignores.

Why Smart Policies Matter for Regulated Data

Data security policies aren’t just an internal safeguard, they are how compliance is enforced in practice. Regulations like GDPR, HIPAA, and PCI DSS require demonstrable control over sensitive data.

Poorly written policies generate alert fatigue, making it harder to detect real violations. Well-crafted ones reduce the risk of noncompliance, streamline audits, and improve breach response.

Recommendations:

  • Map each policy directly to a specific regulatory requirement.
  • Retire rules that create noise without reducing actual risk.

💡 Tip: If a policy doesn’t map to a regulation or a real risk, it’s adding effort without adding value.

Making Policy Creation Simple, Powerful, and Built for Results 

An effective solution for policy creation should make it easy to get started, provide the flexibility to adapt to your unique environment, and give you the deep data context you need to make policies that actually work. It should streamline the process so you can move quickly without sacrificing control, compliance, or clarity.

Sentra is that solution. By combining intuitive policy building with deep data context, Sentra simplifies and strengthens the entire lifecycle of policy creation.

With Sentra, you can:

  • Start fast with out-of-the-box, low-noise controls.
  • Create custom policies without complexity.
  • Leverage real-time knowledge of where sensitive data lives and who has access to it.
  • Continuously tune for low noise with performance metrics.
  • Understand which regulations you can adhere to

💡 Tip: The true value of a policy isn’t how often it triggers, it’s whether it consistently drives the right response.

Good Policies Start with Good Visibility

The best data security policies are written by teams who know exactly where sensitive data lives, how it moves, who can access it, and what creates risk. Without that visibility, policy writing becomes guesswork. With it, enforcement becomes simple, effective, and sustainable.

At Sentra, we believe policy creation should be driven by real data, not assumptions. If you’re ready to move from reactive alerts to meaningful control.

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Nikki Ralston
Nikki Ralston
Gilad Golani
Gilad Golani
September 3, 2025
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Data Loss Prevention

Supercharging DLP with Automatic Data Discovery & Classification of Sensitive Data

Supercharging DLP with Automatic Data Discovery & Classification of Sensitive Data

Data Loss Prevention (DLP) is a keystone of enterprise security, yet traditional DLP solutions continue to suffer from high rates of both false positives and false negatives, primarily because they struggle to accurately identify and classify sensitive data in cloud-first environments.

New advanced data discovery and contextual classification technology directly addresses this gap, transforming DLP from an imprecise, reactive tool into a proactive, highly effective solution for preventing data loss.

Why DLP Solutions Can’t Work Alone

DLP solutions are designed to prevent sensitive or confidential data from leaving your organization, support regulatory compliance, and protect intellectual property and reputation. A noble goal indeed.  Yet DLP projects are notoriously anxiety-inducing for CISOs. On the one hand,  they often generate a high amount of false positives that disrupt legitimate business activities and further exacerbate alert fatigue for security teams.

What’s worse than false positives? False negatives. Today traditional DLP solutions too often fail to prevent data loss because they cannot efficiently discover and classify sensitive data in dynamic, distributed, and ephemeral cloud environments.

Traditional DLP faces a twofold challenge: 

  • High False Positives: DLP tools often flag benign or irrelevant data as sensitive, overwhelming security teams with unnecessary alerts and leading to alert fatigue.

  • High False Negatives: Sensitive data is frequently missed due to poor or outdated classification, leaving organizations exposed to regulatory, reputational, and operational risks.

These issues stem from DLP’s reliance on basic pattern-matching, static rules, and limited context. As a result, DLP cannot keep pace with the ways organizations use, store, and share data, resulting in the dual-edged sword of both high false positives and false negatives. Furthermore, the explosion of unstructured data types and shadow IT creates blind spots that traditional DLP solutions cannot detect. As a result, DLP often can’t  keep pace with the ways organizations use, store, and share data. It isn’t that DLP solutions don’t work, rather they lack the underlying discovery and classification of sensitive data needed to work correctly.

AI-Powered Data Discovery & Classification Layer

Continuous, accurate data classification is the foundation for data security. An AI-powered data discovery and classification platform can act as the intelligence layer that makes DLP work as intended. Here’s how Sentra complements the core limitations of DLP solutions:

1. Continuous, Automated Data Discovery

  • Comprehensive Coverage: Discovers sensitive data across all data types and locations - structured and unstructured sources, databases, file shares, code repositories, cloud storage, SaaS platforms, and more.

  • Cloud-Native & Agentless: Scans your entire cloud estate (AWS, Azure, GCP, Snowflake, etc.) without agents or data leaving your environment, ensuring privacy and scalability.
  • Shadow Data Detection: Uncovers hidden or forgotten (“shadow”) data sets that legacy tools inevitably miss, providing a truly complete data inventory.

2. Contextual, Accurate Classification

  • AI-Driven Precision: Sentra proprietary LLMs and hybrid models achieve over 95% classification accuracy, drastically reducing both false positives and false negatives.

  • Contextual Awareness: Sentra goes beyond simple pattern-matching to truly understand business context, data lineage, sensitivity, and usage, ensuring only truly sensitive data is flagged for DLP action.
  • Custom Classifiers: Enables organizations to tailor classification to their unique business needs, including proprietary identifiers and nuanced data types, for maximum relevance.

3. Real-Time, Actionable Insights

  • Sensitivity Tagging: Automatically tags and labels files with rich metadata, which can be fed directly into your DLP for more granular, context-aware policy enforcement.

  • API Integrations: Seamlessly integrates with existing DLP, IR, ITSM, IAM, and compliance tools, enhancing their effectiveness without disrupting existing workflows.
  • Continuous Monitoring: Provides ongoing visibility and risk assessment, so your DLP is always working with the latest, most accurate data map.

How Sentra Supercharges DLP Solutions

How Sentra supercharges DLP solutions

Better Classification Means Less Noise, More Protection

  • Reduce Alert Fatigue: Security teams focus on real threats, not chasing false alarms, which results in better resource allocation and faster response times.

  • Accelerate Remediation: Context-rich alerts enable faster, more effective incident response, minimizing the window of exposure.

  • Regulatory Compliance: Accurate classification supports GDPR, PCI DSS, CCPA, HIPAA, and more, reducing audit risk and ensuring ongoing compliance.

  • Protect IP and Reputation: Discover and secure proprietary data, customer information, and business-critical assets, safeguarding your organization’s most valuable resources.

Why Sentra Outperforms Legacy Approaches

Sentra’s hybrid classification framework combines rule-based systems for structured data with advanced LLMs and zero-shot learning for unstructured and novel data types.

This versatility ensures:

  • Scalability: Handles petabytes of data across hybrid and multi-cloud environments, adapting as your data landscape evolves.
  • Adaptability: Learns and evolves with your business, automatically updating classifications as data and usage patterns change.
  • Privacy: All scanning occurs within your environment - no data ever leaves your control, ensuring compliance with even the strictest data residency requirements.

Use Case: Where DLP Alone Fails, Sentra Prevails

A financial services company uses a leading DLP solution to monitor and prevent the unauthorized sharing of sensitive client information, such as account numbers and tax IDs, across cloud storage and email. The DLP is configured with pattern-matching rules and regular expressions for identifying sensitive data.

What Goes Wrong:


An employee uploads a spreadsheet to a shared cloud folder. The spreadsheet contains a mix of client names, account numbers, and internal project notes. However, the account numbers are stored in a non-standard format (e.g., with dashes, spaces, or embedded within other text), and the file is labeled with a generic name like “Q2_Projects.xlsx.” The DLP solution, relying on static patterns and file names, fails to recognize the sensitive data and allows the file to be shared externally. The incident goes undetected until a client reports a data breach.

How Sentra Solves the Problem:


To address this, the security team set out to find a solution capable of discovering and classifying unstructured data without creating more overhead. They selected Sentra for its autonomous ability to continuously discover and classify all types of data across their hybrid cloud environment. Once deployed, Sentra immediately recognizes the context and content of files like the spreadsheet that enabled the data leak. It accurately identifies the embedded account numbers—even in non-standard formats—and tags the file as highly sensitive.

This sensitivity tag is automatically fed into the DLP, which then successfully enforces strict sharing controls and alerts the security team before any external sharing can occur. As a result, all sensitive data is correctly classified and protected, the rate of false negatives was dramatically reduced, and the organization avoids further compliance violations and reputational harm.

Getting Started with Sentra is Easy

  1. Deploy Agentlessly: No complex installation. Sentra integrates quickly and securely into your environment, minimizing disruption.

  2. Automate Discovery & Classification: Build a living, accurate inventory of your sensitive data assets, continuously updated as your data landscape changes.

  3. Enhance DLP Policies: Feed precise, context-rich sensitivity tags into your DLP for smarter, more effective enforcement across all channels.

  4. Monitor Continuously: Stay ahead of new risks with ongoing discovery, classification, and risk assessment, ensuring your data is always protected.

“Sentra’s contextual classification engine turns DLP from a reactive compliance checkbox into a proactive, business-enabling security platform.”

Fuel DLP with Automatic Discovery & Classification

DLP is an essential data protection tool, but without accurate, context-aware data discovery and classification, it’s incomplete and often ineffective. Sentra supercharges your DLP with continuous data discovery and accurate classification, ensuring you find and protect what matters most—while eliminating noise, inefficiency, and risk. 

Ready to see how Sentra can supercharge your DLP? Contact us for a demo today.

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Veronica Marinov
Veronica Marinov
Romi Minin
Romi Minin
May 15, 2025
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AI and ML

Ghosts in the Model: Uncovering Generative AI Risks

Ghosts in the Model: Uncovering Generative AI Risks

Generative AI risks are no longer hypothetical. They’re shaping the way enterprises think about cloud security. As artificial intelligence (AI) becomes deeply integrated into enterprise workflows, organizations are increasingly leveraging cloud-based AI services to enhance efficiency and decision-making.

In 2024, 56% of organizations adopted AI to develop custom applications, with 39% of Azure users leveraging Azure OpenAI services. However, with rapid AI adoption in cloud environments, security risks are escalating. As AI continues to shape business operations, the security and privacy risks associated with cloud-based AI services must not be overlooked. Understanding these risks (and how to mitigate them) is essential for organizations looking to protect their proprietary models and sensitive data.

‍Types of Generative AI Risks in Cloud Environments

When discussing AI services in cloud environments, there are two primary types of services that introduce different types of security and privacy risks. This article dives into these risks and explores best practices to mitigate them, ensuring organizations can leverage AI securely and effectively.

1. Data Exposure and Access Risks in Generative AI Platforms

Examples include OpenAI, Google, Meta, and Microsoft, which develop large-scale AI models and provide AI-related services, such as Azure OpenAI, Amazon Bedrock, Google’s Bard, Microsoft Copilot Studio. These services allow organizations to build AI Agents and GenAI services that  are designed to help users perform tasks more efficiently by integrating with existing tools and platforms. For instance, Microsoft Copilot can provide writing suggestions, summarize documents, or offer insights within platforms like Word or Excel, though securing regulated data in Microsoft 365 Copilot requires specific security considerations..

What is RAG (Retrieval-Augmented Generation)?

Many AI systems use Retrieval-Augmented Generation (RAG) to improve accuracy. Instead of solely relying on a model’s pre-trained knowledge, RAG allows the system to fetch relevant data from external sources, such as a vector database, using algorithms like k-nearest neighbor. This retrieved information is then incorporated into the model’s response.

When used in enterprise AI applications, RAG enables AI agents to provide contextually relevant responses. However, it also introduces a risk - if access controls are too broad, users may inadvertently gain access to sensitive corporate data.

How Does RAG (Retrieval-Augmented Generation) Apply to AI Agents?

In AI agents, RAG is typically used to enhance responses by retrieving relevant information from a predefined knowledge base.

Example: In AWS Bedrock, you can define a serverless vector database in OpenSearch as a knowledge base for a custom AI agent. This setup allows the agent to retrieve and incorporate relevant context dynamically, effectively implementing RAG.

Generative AI Risks and Security Threats of AI Platforms

Custom generative AI applications, such as AI agents or enterprise-built copilots, are often integrated with organizational knowledge bases like Amazon S3, SharePoint, Google Drive, and other data sources. While these models are typically not directly trained on sensitive corporate data, the fact that they can access these sources creates significant security risks.

One potential generative AI risk is data exposure through prompts, but this only arises under certain conditions. If access controls aren’t properly configured, users interacting with AI agents might unintentionally or maliciously - prompt the model to retrieve confidential or private information.This isn’t limited to cleverly crafted prompts; it reflects a broader issue of improper access control and governance.

Configuration and Access Control Risks

The configuration of the AI agent is a critical factor. If an agent is granted overly broad access to enterprise data without proper role-based restrictions, it can return sensitive information to users who lack the necessary permissions. For instance, a model connected to an S3 bucket with sensitive customer data could expose that data if permissions aren’t tightly controlled. Simple misconfigurations can lead to serious data exposure incidents, even in applications designed for security.

A common scenario might involve an AI agent designed for Sales that has access to personally identifiable information (PII) or customer records. If the agent is not properly restricted, it could be queried by employees outside of Sales, such as developers - who should not have access to that data.

Example Generative AI Risk Scenario

An employee asks a Copilot-like agent to summarize company-wide sales data. The AI returns not just high-level figures, but also sensitive customer or financial details that were unintentionally exposed due to lax access controls.

Challenges in Mitigating Generative AI Risks

The core challenge, particularly relevant to platforms like Sentra, is enforcing governance to ensure only appropriate data is used and accessible by AI services.

This includes:

  • Defining and enforcing granular data access controls.
  • Preventing misconfigurations or overly permissive settings.
  • Maintaining real-time visibility into which data sources are connected to AI models.
  • Continuously auditing data flows and access patterns to prevent leaks.

Without rigorous governance and monitoring, even well-intentioned GenAI implementations can lead to serious data security incidents.

2. ML and AI Studios for Building New Models

Many companies, such as large financial institutions, build their own AI and ML models to make better business decisions, or to improve their user experiences. Unlike large foundational models from major tech companies, these custom AI models are trained by the organization itself on their applications or corporate data.

Security Risks of Custom AI Models

  1. Weak Data Governance Policies - If data governance policies are inadequate, sensitive information, such as customers' Personally Identifiable Information (PII), could be improperly accessed or shared during the training process. This can lead to data breaches, privacy compliance violations, and unethical AI usage. The growing recognition of generative AI-related risks has driven the development of more AI compliance frameworks that are now being actively enforced with significant penalties..
  2. Excessive Access to Training Data and AI Models - Granting unrestricted access to training datasets and machine learning (ML)/AI models increases the risk of data leaks and misuse. Without proper access controls, sensitive data used in training can be exposed to unauthorized individuals, leading to compliance and security concerns.
  3. AI Agents Exposing Sensitive Data -  AI agents that do not have proper safeguards can inadvertently expose sensitive information to a broad audience within an organization. For example, an employee could retrieve confidential data such as the CEO’s salary or employment contracts if access controls are not properly enforced.
  4. Insecure Model Storage – Once a model is trained, it is typically stored in the same environment (e.g., in Amazon SageMaker, the training job stores the trained model in S3). If not properly secured, proprietary models could be exposed to unauthorized access, leading to risks such as model theft.
  5. Deployment Vulnerabilities – A lack of proper access controls can result in unauthorized use of AI models. Organizations need to assess who has access: Is the model public? Can external entities interact with or exploit it?

Shadow AI and Forgotten Assets – AI models or artifacts that are not actively monitored or properly decommissioned can become a security risk. These overlooked assets can serve as attack vectors if discovered by malicious actors.

Example Risk Scenario

A bank develops an AI-powered feature that predicts a customer’s likelihood of repaying a loan based on inputs like financial history, employment status, and other behavioral indicators. While this feature is designed to enhance decision-making and customer experience, it introduces significant generative AI risk if not properly governed.

During development and training, the model may be exposed to personally identifiable information (PII), such as names, addresses, social security numbers, or account details, which is not necessary for the model’s predictive purpose.

⚠️ Best practice: Models should be trained only on the minimum necessary data required for performance, excluding direct identifiers unless absolutely essential. This reduces both privacy risk and regulatory exposure.

If the training pipeline fails to properly separate or mask this PII, the model could unintentionally leak sensitive information. For example, when responding to an end-user query, the AI might reference or infer details from another individual’s record - disclosing sensitive customer data without authorization.

This kind of data leakage, caused by poor data handling or weak governance during training, can lead to serious regulatory non-compliance, including violations of GDPR, CCPA, or other privacy frameworks.

Common Risk Mitigation Strategies and Their Limitations

Many organizations attempt to manage generative AI-related risks through employee training and awareness programs. Employees are taught best practices for handling sensitive data and using AI tools responsibly.
While valuable, this approach has clear limitations:

  • Training Alone Is Insufficient:
    Human error remains a major risk factor, even with proper training. Employees may unintentionally connect sensitive data sources to AI models or misuse AI-generated outputs.

  • Lack of Automated Oversight:
    Most organizations lack robust, automated systems to continuously monitor how AI models use data and to enforce real-time security policies. Manual review processes are often too slow and incomplete to catch complex data access risks in dynamic, cloud-based AI environments.
  • Policy Gaps and Visibility Challenges:
    Organizations often operate with multiple overlapping data layers and services. Without clear, enforceable policies, especially automated ones - certain data assets may remain unscanned or unprotected, creating blind spots and increasing risk.

Reducing AI Risks with Sentra’s Comprehensive Data Security Platform

Managing generative AI risks in the cloud requires more than employee training.
Organizations need to adopt robust data governance frameworks and data security platforms (like Sentra’s) that address the unique challenges of AI.

This includes:

  • Discovering AI Assets: Automatically identify AI agents, knowledge bases, datasets, and models across the environment.
  • Classifying Sensitive Data: Use automated classification and tagging to detect and label sensitive information accurately.
    Monitoring AI Data Access: Detect which AI agents and models are accessing sensitive data, or using it for training - in real time.
  • Enforcing Access Governance: Govern AI integrations with knowledge bases by role, data sensitivity, location, and usage to ensure only authorized users can access training data, models, and artifacts.
  • Automating Data Protection: Apply masking, encryption, access controls, and other protection methods through automated remediation capabilities across data and AI artifacts used in training and inference processes.

By combining strong technical controls with ongoing employee training, organizations can significantly reduce the risks associated with AI services and ensure compliance with evolving data privacy regulations.

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