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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.

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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Best Practices: Automatically Tag and Label Sensitive Data

Best Practices: Automatically Tag and Label Sensitive Data

The Importance of Data Labeling and Tagging

In today's fast-paced business environment, data rarely stays in one place. It moves across devices, applications, and services as individuals collaborate with internal teams and external partners. This mobility is essential for productivity but poses a challenge: how can you ensure your data remains secure and compliant with business and regulatory requirements when it's constantly on the move?

Why Labeling and Tagging Data Matters

Data labeling and tagging provide a critical solution to this challenge. By assigning sensitivity labels to your data, you can define its importance and security level within your organization. These labels act as identifiers that abstract the content itself, enabling you to manage and track the data type without directly exposing sensitive information. With the right labeling, organizations can also control access in real-time.

For example, labeling a document containing social security numbers or credit card information as Highly Confidential allows your organization to acknowledge the data's sensitivity and enforce appropriate protections, all without needing to access or expose the actual contents.

Why Sentra’s AI-Based Classification Is a Game-Changer

Sentra’s AI-based classification technology enhances data security by ensuring that the sensitivity labels are applied with exceptional accuracy. Leveraging advanced LLM models, Sentra enhances data classification with context-aware capabilities, such as:

  • Detecting the geographic residency of data subjects.
  • Differentiating between Customer Data and Employee Data.
  • Identifying and treating Synthetic or Mock Data differently from real sensitive data.

This context-based approach eliminates the inefficiencies of manual processes and seamlessly scales to meet the demands of modern, complex data environments. By integrating AI into the classification process, Sentra empowers teams to confidently and consistently protect their data—ensuring sensitive information remains secure, no matter where it resides or how it is accessed.

Benefits of Labeling and Tagging in Sentra

Sentra enhances your ability to classify and secure data by automatically applying sensitivity labels to data assets. By automating this process, Sentra removes the manual effort required from each team member—achieving accuracy that’s only possible through a deep understanding of what data is sensitive and its broader context.

Here are some key benefits of labeling and tagging in Sentra:

  1. Enhanced Security and Loss Prevention: Sentra’s integration with Data Loss Prevention (DLP) solutions prevents the loss of sensitive and critical data by applying the right sensitivity labels. Sentra’s granular, contextual tags help to provide the detail necessary to action remediation automatically so that operations can scale.
  2. Easily Build Your Tagging Rules: Sentra’s Intuitive Rule Builder allows you to automatically apply sensitivity labels to assets based on your pre-existing tagging rules and or define new ones via the builder UI (see screen below). Sentra imports discovered Microsoft Purview Information Protection (MPIP) labels to speed this process.
  1. Labels Move with the Data: Sensitivity labels created in Sentra can be mapped to Microsoft Purview Information Protection (MPIP) labels and applied to various applications like SharePoint, OneDrive, Teams, Amazon S3, and Azure Blob Containers. Once applied, labels are stored as metadata and travel with the file or data wherever it goes, ensuring consistent protection across platforms and services.
  2. Automatic Labeling: Sentra allows for the automatic application of sensitivity labels based on the data's content. Auto-tagging rules, configured for each sensitivity label, determine which label should be applied during scans for sensitive information.
  3. Support for Structured and Unstructured Data: Sentra enables labeling for files stored in cloud environments such as Amazon S3 or EBS volumes and for database columns in structured data environments like Amazon RDS. By implementing these labeling practices, your organization can track, manage, and protect data with ease while maintaining compliance and safeguarding sensitive information. Whether collaborating across services or storing data in diverse cloud environments, Sentra ensures your labels and protection follow the data wherever it goes.

Applying Sensitivity Labels to Data Assets in Sentra

In today’s rapidly evolving data security landscape, ensuring that your data is properly classified and protected is crucial. One effective way to achieve this is by applying sensitivity labels to your data assets. Sensitivity labels help ensure that data is handled according to its level of sensitivity, reducing the risk of accidental exposure and enabling compliance with data protection regulations.

Below, we’ll walk you through the necessary steps to automatically apply sensitivity labels to your data assets in Sentra. By following these steps, you can enhance your data governance, improve data security, and maintain clear visibility over your organization's sensitive information.

The process involves three key actions:

  1. Create Sensitivity Labels: The first step in applying sensitivity labels is creating them within Sentra. These labels allow you to categorize data assets according to various rules and classifications. Once set up, these labels will automatically apply to data assets based on predefined criteria, such as the types of classifications detected within the data. Sensitivity labels help ensure that sensitive information is properly identified and protected.
  2. Connect Accounts with Data Assets: The next step is to connect your accounts with the relevant data assets. This integration allows Sentra to automatically discover and continuously scan all your data assets, ensuring that no data goes unnoticed. As new data is created or modified, Sentra will promptly detect and categorize it, keeping your data classification up to date and reducing manual efforts.
  3. Apply Classification Tags: Whenever a data asset is scanned, Sentra will automatically apply classification tags to it, such as data classes, data contexts, and sensitivity labels. These tags are visible in Sentra’s data catalog, giving you a comprehensive overview of your data’s classification status. By applying these tags consistently across all your data assets, you’ll have a clear, automated way to manage sensitive data, ensuring compliance and security.

By following these steps, you can streamline your data classification process, making it easier to protect your sensitive information, improve your data governance practices, and reduce the risk of data breaches.

Applying MPIP Labels

In order to apply Microsoft Purview Information Protection (MPIP) labels based on Sentra sensitivity labels, you are required to follow a few additional steps:

  1. Set up the Microsoft Purview integration - which will allow Sentra to import and sync MPIP sensitivity labels.
  2. Create tagging rules - which will allow you to map Sentra sensitivity labels to MPIP sensitivity labels (for example “Very Confidential” in Sentra would be mapped to “ACME - Highly Confidential” in MPIP), and choose to which services this rule would apply (for example, Microsoft 365 and Amazon S3).

Using Sensitivity Labels in Microsoft DLP

Microsoft Purview DLP (as well as all other industry-leading DLP solutions) supports MPIP labels in its policies so admins can easily control and prevent data loss of sensitive data across multiple services and applications.For instance, a MPIP ‘highly confidential’ label may instruct Microsoft Purview DLP to restrict transfer of sensitive data outside a certain geography. Likewise, another similar label could instruct that confidential intellectual property (IP) is not allowed to be shared within Teams collaborative workspaces.Labels can be used to help control access to sensitive data as well. Organizations can set a rule with read permission only for specific tags. For example, only production IAM roles can access production files. Further, for use cases where data is stored in a single store, organizations can estimate the storage cost for each specific tag.

Build a Stronger Foundation with Accurate Data Classification

Effectively tagging sensitive data unlocks significant benefits for organizations, driving improvements across accuracy, efficiency, scalability, and risk management. With precise classification exceeding 95% accuracy and minimal false positives, organizations can confidently label both structured and unstructured data. Automated tagging rules reduce the reliance on manual effort, saving valuable time and resources. Granular, contextual tags enable confident and automated remediation, ensuring operations can scale seamlessly. Additionally, robust data tagging strengthens DLP and compliance strategies by fully leveraging Microsoft Purview’s capabilities. By streamlining these processes, organizations can consistently label and secure data across their entire estate, freeing resources to focus on strategic priorities and innovation.

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Aviv Zisso
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Achieving Exabyte Scale Enterprise Data Security

Achieving Exabyte Scale Enterprise Data Security

The Growing Challenge for Enterprise Data Security

Enterprises are facing a unique set of challenges when it comes to managing and protecting their data. From my experience with customers, I’ve seen these challenges intensify as data governance frameworks struggle to keep up with evolving environments. Data is not confined to a single location - it’s scattered across different environments, from cloud platforms to on-premises servers and various SaaS applications. This distributed and siloed data stores model, while beneficial for flexibility and scalability, complicates data governance and introduces new security and privacy risks.

Many organizations now manage petabytes of constantly changing information, with new data being created, updated, or shared every second. As this volume expands into the hundreds or even thousands of petabytes (exabytes!), keeping track of it all becomes an overwhelming challenge.

The situation is further complicated by the rapid movement of data. Employees and applications copy, modify, or relocate sensitive information in seconds, often across diverse environments. This includes on-premises systems, multiple cloud platforms, and technologies like PaaS and IaaS. Such rapid data sprawl makes it increasingly difficult to maintain visibility and control over the data, and to keep the data protected with all the required controls, such as encryption and access controls.

The Complexities of Access Control

Alongside data sprawl, there’s also the challenge of managing access. Enterprise data ecosystems support thousands of identities (users, apps, machines) each with different levels of access and permissions. These identities may be spread across multiple departments and accounts, and their data needs are constantly evolving. Tracking and controlling which identity can access which data sets becomes a complex puzzle, one that can expose an organization to risks if not handled with precision.

For any enterprise, having an accurate, up-to-date view of who or what has access to what data (and why) is essential to maintaining security and ensuring compliance. Without this visibility and control, organizations run the risk of unauthorized access and potential data breaches.

The Need for Automated Data Risk Assessment 

In today’s data-driven world, security analysts often discover sensitive data in misconfigured environments—sometimes only after a breach—leading to a time-consuming process of validating data sensitivity, identifying business owners, and initiating remediation. In my work with enterprises, I’ve noticed this process is often further complicated by unclear ownership and inconsistent remediation practices.

With data constantly moving and accessed across diverse environments, organizations face critical questions: 

  • Where is our sensitive data?
  • Who has access? 
  • Are we compliant? 

Addressing these challenges requires a dynamic, always-on approach with trusted classification and automated remediation to monitor risks and enforce protection 24/7.

The Scale of the Problem

For enterprise organizations, scale amplifies every data management challenge. The larger the organization, the more complex it becomes to ensure data visibility, secure access, and maintain compliance. Traditional, human-dependent security approaches often struggle to keep up, leaving gaps that malicious actors exploit. Enterprises need robust, scalable solutions that can adapt to their expanding data needs and provide real-time insights into where sensitive data resides, how it’s used, and where the risks lie.

The Solution: Data Security Platform (DSP)

Sentra’s Cloud-native Data Security Platform (DSP) provides a solution designed to meet these challenges head-on. By continuously identifying sensitive data, its posture, and access points, DSP gives organizations complete control over their data landscape.

Sentra enables security teams to gain full visibility and control of their data while proactively protecting against sensitive data breaches across the public cloud. By locating all data, properly classifying its sensitivity, analyzing how it’s secured (its posture), and monitoring where it’s moving, Sentra helps reduce the “data attack surface” - the sum of all places where sensitive or critical data is stored.

Based on a cloud-native design, Sentra’s platform combines robust capabilities, including Data Discovery and Classification, Data Security Posture Management (DSPM), Data Access Governance (DAG), and Data Detection and Response (DDR). This comprehensive approach to data security ensures that Sentra’s customers can achieve enterprise-scale protection and gain crucial insights into their data. Sentra’s DSP offers a distinct layer of data protection that goes beyond traditional, infrastructure-dependent approaches, making it an essential addition to any organization’s security strategy.

By scaling data protection across multiple clouds and on-premises, Sentra enables organizations to meet the demands of enterprise growth and keep up with evolving business needs. And it does so efficiently, without creating unnecessary burdens on the security teams managing it.

determine the sensitivity of the data timeline

How a Robust DSP Can Handle Scale Efficiently

When selecting a DSP solution, it's essential to consider: How does this product ensure your sensitive data is kept secure no matter where it moves? And how can it scale effectively without driving up costs by constantly combing through every bit of data?

The key is in tailoring the DSP to your unique needs. Each organization, with its variety of environments and security requirements, needs a DSP that can adapt to specific demands. At Sentra, we’ve developed a flexible scanning engine that puts you in control, allowing you to customize what data is scanned, how it is tagged, and when. Our platform incorporates advanced optimization algorithms to keep scanning costs low without compromising on quality.

Priority Scanning

Do you really need to scan all the organization’s data? Do all data stores and assets hold the same priority? A smart DLP solution puts you in control, allowing you to adjust your scanning strategy based on the organization's specific priorities and sensitive data locations and uses. 

For example, some organizations may prioritize scanning employee-generated content, while others might focus on their production environment and perform more frequent scans there. Tailoring your scanning strategy ensures that the most important data is protected without overwhelming resources.

Smart Sampling

Is it necessary to scan every database record and every character in every file? The answer depends on your organization’s risk tolerance. For instance, in a PCI production environment, you might reduce the amount of sampling and scan every byte, while in a development environment you can group and sample data sets that share similar characteristics, allowing for more efficient scanning without compromising on security.

Edit Scan Configuration for data warehouse bucket

Delta scanning (tracking data changes) 

Delta scanning focuses on what matters most by selectively scanning data that poses a higher risk. Instead of re-scanning data that hasn’t changed, delta scanning prioritizes new or modified data, ensuring that resources are used efficiently. This approach helps to reduce scanning costs while keeping your data protection efforts focused on what has changed or been added.

A smart DLP will run efficiently and prioritize “new data” over “old data”, allowing you to optimize your scanning costs.  

On-Demand Data Scans

As you build your scanning strategy, it is important to keep the ability to trigger an immediate scan request. This is handy when you’re fixing security risks and want a short feedback loop to verify your changes. 

This also gives you the ability to prepare for compliance audits effectively by ensuring readiness and accurate and fresh classification.

Data warehouse bucket from Sentra's data security platform

Balancing Scan Speed and Cost

Smart sampling enables a balance between scan speed and cost. By focusing scans on relevant data and optimizing the scanning process, you can keep costs down while maintaining high accuracy and efficiency across your data landscape.

Achieve Scalable Data Protection with Cloud-Native DSPs

As enterprise organizations continue to navigate the complexities of managing vast amounts of data across multiple environments, the need for effective data security strategies becomes increasingly critical. The challenges of access control, risk analysis, and scaling security efforts can overwhelm traditional approaches, making it clear that a more automated, comprehensive solution is essential. A cloud-native Data Security Platform (DSP) offers the agility and efficiency required to meet these demands. 

By incorporating advanced features like smart sampling, delta scanning, and on-demand scan requests, Sentra’s DSP ensures that organizations can continuously monitor, protect, and optimize their data security posture without unnecessary resource strain. Balancing scan frequency, sensitivity and cost efficiency further enhances the ability to scale effectively, providing organizations with the tools they need to manage data risks, remain compliant, and protect sensitive information in an ever-evolving digital landscape.

If you want to learn more, talk to our data security experts and request a demo today.

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David Stuart
October 21, 2024
5
Min Read
Data Sprawl

How Sentra Built a Data Security Platform for the AI Era

How Sentra Built a Data Security Platform for the AI Era

In just three years, Sentra has witnessed the rapid evolution of the data security landscape. What began with traditional on-premise Data Loss Prevention (DLP) solutions has shifted to a cloud-native focus with Data Security Posture Management (DSPM). This marked a major leap in how organizations protect their data, but the evolution didn’t stop there.

The next wave introduced new capabilities like Data Detection and Response (DDR) and Data Access Governance (DAG), pushing the boundaries of what DSPM could offer. Now, we’re entering an era where SaaS Security Posture Management (SSPM) and Artificial Intelligence Security Posture Management (AI-SPM) are becoming increasingly important. 

These shifts are redefining what we’ve traditionally called Data Security Platform (DSP) solutions, marking a significant transformation in the industry. The speed of this evolution speaks to the growing complexity of data security needs and the innovation required to meet them.

The Evolution of Data Security

What Is Driving The Evolution of Data Security?

The evolution of the data security market is being driven by several key macro trends:

  • Digital Transformation and Data Democratization: Organizations are increasingly embracing digital transformation, making data more accessible to various teams and users.
  • Rapid Cloud Adoption: Businesses are moving to the cloud at an unprecedented pace to enhance agility and responsiveness.
  • Explosion of Siloed Data Stores: The growing number of siloed data stores, diverse data technologies, and an expanding user base is complicating data management.
  • Increased Innovation Pace: The rise of artificial intelligence (AI) is accelerating the pace of innovation, creating new opportunities and challenges in data security.
  • Resource Shortages: As organizations grow, the need for automation to keep up with increasing demands has never been more critical.
  • Stricter Data Privacy Regulations: Heightened data privacy laws and stricter breach disclosure requirements are adding to the urgency for robust data protection measures.
Rapid cloud adoption

Similarly, there has been an evolution in the roles involved with the management, governance, and protection of data. These roles are increasingly intertwined and co-dependent as described in our recent blog entitled “Data: The Unifying Force Behind Disparate GRC Functions”. We identify that today each respective function operates within its own domain yet shares ownership of data at its core. As the co-dependency on data increases so does the need for a unifying platform approach to data security.

Sentra has adapted to these changes to align our messaging with industry expectations, buyer requirements, and product/technology advancements.

A Data Security Platform for the AI Era

Sentra is setting the standard with the leading Data Security Platform for the AI Era.

With its cloud-native design, Sentra seamlessly integrates powerful capabilities like Data Discovery and Classification, Data Security Posture Management (DSPM), Data Access Governance (DAG), and Data Detection and Response (DDR) into a comprehensive solution. This allows our customers to achieve enterprise-scale data protection while addressing critical questions about their data.

data security cycle - visibility, context, access, risks, threats

What sets Sentra apart is its connector-less, cloud-native architecture, which effortlessly scales to accommodate multi-petabyte, multi-cloud environments without the administrative burdens typical of connector-based legacy systems. These more labor-intensive approaches often struggle to keep pace and frequently overlook shadow data.

Moreover, Sentra harnesses the power of AI and machine learning to accurately interpret data context and classify data. This not only enhances data security but also ensures the privacy and integrity of data used in Gen- AI applications. We recognized the critical need for accurate and automated Data Discovery and Classification, along with Data Security Posture Management (DSPM), to address the risks associated with data proliferation in a multi-cloud landscape. Based on our customers' evolving needs, we expanded our capabilities to include DAG and DDR. These tools are essential for managing data access, detecting emerging threats, and improving risk mitigation and data loss prevention.

DAG maps the relationships between cloud identities, roles, permissions, data stores, and sensitive data classes. This provides a complete view of which identities and data stores in the cloud may be overprivileged. Meanwhile, DDR offers continuous threat monitoring for suspicious data access activity, providing early warnings of potential breaches.

We grew to support SaaS data repositories including Microsoft 365 (SharePoint, OneDrive, Teams, etc.), G Suite (Gdrive) and leveraged AI/ML to accurately classify data hidden within unstructured data stores.

Sentra’s accurate data sensitivity tagging and granular contextual details allows organizations to enhance the effectiveness of their existing tools, streamline workflows, and automate remediation processes. Additionally, Sentra offers pre-built integrations with various analysis and response tools used across the enterprise, including data catalogs, incident response (IR) platforms, IT service management (ITSM) systems, DLPs, CSPMs, CNAPPs, IAM, and compliance management solutions.

How Sentra Redefines Enterprise Data Security Across Clouds

Sentra has architected a solution that can deliver enterprise-scale data security without the traditional constraints and administrative headaches. Sentra’s cloud-native design easily scales to petabyte data volumes across multi-cloud and on-premises environments. 

The Sentra platform incorporates a few major differentiators that distinguish it from other solutions including:

  • Novel Scanning Technology: Sentra uses inventory files and advanced automatic grouping to create a new entity called “Data Asset”, a group of files that have the same structure, security posture and business function. Sentra automatically reduces billions of files into thousands of data assets (that represent different types of data) continuously, enabling full coverage of 100% of cloud data of petabytes to just several hundreds of thousands of files which need to be scanned (5-6 orders of magnitude less scanning required). Since there is no random sampling involved in the process, all types of data are fully scanned and for differentials on a daily basis. Sentra supports all leading IaaS, PaaS, SaaS and On-premises stores.
  • AI-powered Autonomous Classification: Sentra’s use of AI-powered classification provides approximately 97% classification accuracy of data within unstructured documents and structured data. Additionally, Sentra provides rich data context (distinct from data class or type) about multiple aspects of files, such as data subject residency, business impact, synthetic or real data, and more. Further, Sentra’s classification uses LLMs (inside the customer environment) to automatically learn and adapt based on the unique business context, false positive user inputs, and allows users to add AI-based classifiers using natural language (powered by LLMs). This autonomous learning means users don’t have to customize the system themselves, saving time and helping to keep pace with dynamic data.
  • Data Perimeters / Movement: Sentra DataTreks™ provides the ability to understand data perimeters automatically and detect when data is moving (e.g. copied partially or fully) to a different perimeter. For example, it can detect data similarity/movement from a well protected production environment to a less- protected development environment. This is important for highly dynamic cloud environments and promoting secure data democratization.
  • Data Detection and Response (DDR): Sentra’s DDR module highlights anomalies such as unauthorized data access or unusual data movements in near real-time, integrating alerts into existing tools like ServiceNow or JIRA for quick mitigation.
  • Easy Customization: In addition to ‘learning’ of a customer's unique data types, with Sentra it’s easy to create new classifiers, modify policies, and apply custom tagging labels.

As AI reshapes the digital landscape, it also creates new vulnerabilities, such as the risk of data exposure through AI training processes. The Sentra platform addresses these AI-specific challenges, while continuing to tackle the persistent security issues from the cloud era, providing an integrated solution that ensures data security remains resilient and adaptive.

Use Cases: Solving Complex Problems with Unique Solutions

Sentra’s unique capabilities allow it to serve a broad spectrum of challenging data security, governance and compliance use cases. Two frequently cited DSPM use cases are preventing data breaches and facilitating GenAI technology deployments. With the addition of data privacy compliance, these represent the top three.  

Let's dive deeper into how Sentra's platform addresses specific challenges:

Data Risk Visibility

Sentra’s Data Security Platform enables continuous analysis of your security posture and automates risk assessments across your entire data landscape. It identifies data vulnerabilities across cloud-native and unmanaged databases, data lakes, and metadata catalogs. By automating the discovery and classification of sensitive data, teams can prioritize actions based on the sensitivity and policy guidelines related to each asset. This automation not only saves time but also enhances accuracy, especially when leveraging large language models (LLMs) for detailed data classification.

Security and Compliance Audit

Sentra Data Security Platform can also automate the process of identifying regulatory violations and ensuring adherence to custom and pre-built policies (including policies that map to common compliance frameworks). 

The platform automates the identification of regulatory violations, ensuring compliance with both custom and established policies. It helps keep sensitive data in the right environments, preventing it from traveling to regions that violate retention policies or lack encryption. Unlike manual policy implementation, which is prone to errors, Sentra’s automated approach significantly reduces the risk of misconfiguration, ensuring that teams don’t miss critical activities.

Data Access Governance

Sentra enhances data access governance (DAG) by enforcing appropriate permissions for all users and applications within an organization. By automating the monitoring of access permissions, Sentra mitigates risks such as excessive permissions and unauthorized access. This ensures that teams can maintain least privilege access control, which is essential in a growing data ecosystem.

Minimizing Data and Attack Surface

The platform’s capabilities also extend to detecting unmanaged sensitive data, such as shadow or duplicate assets. By automatically finding and classifying these unknown data points, Sentra minimizes the attack surface, controls data sprawl, and enhances overall data protection.

Secure and Responsible AI

As organizations build new Generative AI applications, Sentra extends its protection to LLM applications, treating them as part of the data attack surface. This proactive management, alongside monitoring of prompts and outputs, addresses data privacy and integrity concerns, ensuring that organizations are prepared for the future of AI technologies.

Insider Risk Management

Sentra effectively detects insider risks by monitoring user access to sensitive information across various platforms. Its Data Detection and Response (DDR) capabilities provide real-time threat detection, analyzing user activity and audit logs to identify unusual patterns.

Data Loss Prevention (DLP)

The platform integrates seamlessly with endpoint DLP solutions to monitor all access activities related to sensitive data. By detecting unauthorized access attempts from external networks, Sentra can prevent data breaches before they escalate, all while maintaining a positive user experience.

Sentra’s robust Data Security Platform offers solutions for these use cases and more, empowering organizations to navigate the complexities of data security with confidence. With a comprehensive approach that combines visibility, governance, and protection, Sentra helps businesses secure their data effectively in today’s dynamic digital environment.

From DSPM to a Comprehensive Data Security Platform

Sentra has evolved beyond being the leading Data Security Posture Management (DSPM) solution; we are now a Cloud-native Data Security Platform (DSP). Today, we offer holistic solutions that empower organizations to locate, secure, and monitor their data against emerging threats. Our mission is to help businesses move faster and thrive in today’s digital landscape.

What sets the Sentra DSP apart is its unique layer of protection, distinct from traditional infrastructure-dependent solutions. It enables organizations to scale their data protection across ever-expanding multi-cloud environments, meeting enterprise demands while adapting to ever-changing business needs—all without placing undue burdens on the teams managing it.

And we continue to progress. In a world rapidly evolving with advancements in AI, the Sentra Data Security Platform stands as the most comprehensive and effective solution to keep pace with the challenges of the AI age. We are committed to developing our platform to ensure that your data security remains robust and adaptive.

 Sentra's Cloud-Native Data Security Platform provides comprehensive data protection for the entire data estate.
 Sentra Cloud-Native Data Security Platform provides comprehensive data protection for the entire data estate.
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