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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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Yair Cohen
January 28, 2025
5
Min Read
Data Security

Data Protection and Classification in Microsoft 365

Data Protection and Classification in Microsoft 365

Imagine the fallout of a single misstep—a phishing scam tricking an employee into sharing sensitive data. The breach doesn’t just compromise information; it shakes trust, tarnishes reputations, and invites compliance penalties. With data breaches on the rise, safeguarding your organization’s Microsoft 365 environment has never been more critical.

Data classification helps prevent such disasters. This article provides a clear roadmap for protecting and classifying Microsoft 365 data. It explores how data is saved and classified, discusses built-in tools for protection, and covers best practices for maintaining  Microsoft 365 data protection.

How Is Data Saved and Classified in Microsoft 365? 

Microsoft 365 stores data across tools and services. For example, emails are stored in Exchange Online, while documents and data for collaboration are found in Sharepoint and Teams, and documents or files for individual users are stored in OneDrive. This data is primarily unstructured—a format ideal for documents and images but challenging for identifying sensitive information.

All of this data is largely stored in an unstructured format typically used for documents and images. This format not only allows organizations to store large volumes of data efficiently; it also enables seamless collaboration across teams and departments. However, as unstructured data cannot be neatly categorized into tables or columns, it becomes cumbersome to discern what data is sensitive and where it is stored. 

To address this, Microsoft 365 offers a data classification dashboard that helps classify data of varying levels of sensitivity and data governed by different regulatory compliance frameworks. But how does Microsoft identify sensitive information with unstructured data? 

Microsoft employs advanced technologies such as RegEx scans, trainable classifiers, Bloom filters, and data classification graphs to identify and classify data as public, internal, or confidential. Once classified, data protection and governance policies are applied based on sensitivity and retention labels.

Data classification is vital for understanding, protecting, and governing data. With your ​​Microsoft 365 data classified appropriately, you can ensure seamless collaboration without risking data exposure.

Why data classification is important
Figure 1: Why data classification is important

Microsoft 365 Data Protection and Classification Tools

Microsoft 365 includes several key tools and frameworks for classifying and securing data. Here are a few. 

Microsoft Purview 

Microsoft Purview is a cornerstone of data classification and protection within Microsoft 365.

Key Features: 

  • Over 200+ prebuilt classifiers and the ability to create custom classifiers tailored to specific business needs.
  • Purview auto-classifies data across Microsoft 365 and other supported apps, such as Adobe Photoshop and Adobe PDF, while users work on them.
  • Sensitivity labels that apply encryption, watermarks, and access restrictions to secure sensitive data.
  • Double Key Encryption to ensure that sensitivity labels persist even when file formats change.
Sensitivity watermarks in M365
Figure 2: Sensitivity watermarks in Microsoft 365 (Source: Microsoft)
Figure 3: Sensitivity labels for information protection policies in Microsoft 365 (Source: Microsoft)

Purview autonomously applies sensitivity labels like "confidential" or "highly confidential" based on preconfigured policies, ensuring optimal access control. These labels persist even when files are shared or converted to other formats, such as from Word to PDF.

Additionally, Purview’s data loss prevention (DLP) policies prevent unauthorized sharing or deletion of sensitive data by flagging and reporting violations in real time. For example, if a sensitive file is shared externally, Purview can immediately block the transfer and alert your security team.

Sensitivity labeling for announcements in M365
Figure 4: Preventing data loss by using sensitivity labels (Source: Microsoft)

Microsoft Defender 

Microsoft Defender for Cloud Apps strengthens security by providing a cloud app discovery window to identify applications accessing data. Once identified, it classifies files within these applications based on sensitivity, applying appropriate protections as per preconfigured policies.

Microsoft Defender for Cloud - data sensitivity classification
Figure 5: Microsoft Defender data sensitivity classification (Source: Microsoft)

Key Features:

  • Data Sensitivity Classification: Defender identifies sensitive files and assigns protection based on sensitivity levels, ensuring compliance and reducing risk. For example, it labels files containing credit card numbers, personal identifiers, or confidential business information with sensitivity classifications like "Highly Confidential."
  • Threat Detection and Response: Defender detects known threats targeted at sensitive data in emails, collaboration tools (like SharePoint and Teams), URLs, file attachments, and OneDrive. If an admin account is compromised, Microsoft Defender immediately spots the threat, disables the account, and notifies your IT team to prevent significant damage.
  • Automation: Defender automates incident response, ensuring that malicious activities are flagged and remediated promptly.

Intune 

Microsoft Intune provides comprehensive device management and data protection, enabling organizations to enforce policies that safeguard sensitive information on both managed and unmanaged smartphones, computers, and other devices.

Key Features:

  • Customizable Compliance Policies: Intune allows organizations to enforce device compliance policies that align with internal and regulatory standards. For example, it can block non-compliant devices from accessing sensitive data until issues are resolved.
  • Data Access Control: Intune disallows employees from accessing corporate data on compromised devices or through insecure apps, such as those not using encryption for emails.
  • Endpoint Security Management: By integrating with Microsoft Defender, Intune provides endpoint protection and automated responses to detected threats, ensuring only secure devices can access your organization’s network.
Endpoint security overview
Figure 6: Intune device management portal (Source: Microsoft)

Intune supports organizations by enabling the creation and enforcement of device compliance policies tailored to both internal and regulatory standards. These policies detect non-compliant devices, issue alerts, and restrict access to sensitive data until compliance is restored. Conditional access ensures that only secure and compliant devices connect to your network.

Microsoft 365-managed apps like Outlook, Word, and Excel. These policies define which apps can access specific data, such as emails, and regulate permissible actions, including copying, pasting, forwarding, and taking screenshots. This layered security approach safeguards critical information while maintaining seamless app functionality.

Does Microsoft have a DLP Solution?

Microsoft 365’s data loss prevention (DLP) policies represent the implementation of the zero-trust framework. These policies aim to prevent oversharing, accidental deletion, and data leaks across Microsoft 365 services, including Exchange Online, SharePoint, Teams, and OneDrive, as well as Windows and macOS devices.

Retention policies, deployed via retention labels, help organizations manage the data lifecycle effectively.These labels ensure that data is retained only as long as necessary to meet compliance requirements, reducing the risks associated with prolonged data storage.

How DLP policies work
Figure 7: How DLP policies work (Source: Microsoft)

What is the Microsoft 365 Compliance Center?

The Microsoft 365 compliance center offers tools to manage policies and monitor data access, ensuring adherence to regulations. For example, DLP policies allow organizations to define specific automated responses when certain regulatory requirements—like GDPR or HIPAA—are violated.

Microsoft Purview Compliance Portal: This portal ensures sensitive data is classified, stored, retained, and used in adherence to relevant compliance regulations. Meanwhile, Microsoft 365’s MPIP ensures that only authorized users can access sensitive information, whether collaborating on Teams or sharing files in SharePoint. Together, these tools enable secure collaboration while keeping regulatory compliance at the forefront.

12 Best Practices for Microsoft 365 Data Protection and Classification

To achieve effective Microsoft 365 data protection and classification, organizations should follow these steps:

  1. Create precise labels, tags, and classification policies; don’t rely solely on prebuilt labels and policies, as definitions of sensitive data may vary by context.
  2. Automate labeling to minimize errors and quickly capture new datasets.
  3. Establish and enforce data use policies and guardrails automatically to reduce risks of data breaches, compliance failures, and insider threat risks. 
  4. Regularly review and update data classification and usage policies to reflect evolving threats, new data storage, and changing compliance laws.o policies must stay up to date to remain effective.
  5. Define context-appropriate DLP policies based on your business needs; factoring in remote work, ease of collaboration, regional compliance standards, etc.
  6. Apply encryption to safeguard data inside and outside your organization.
  7. Enforce role-based access controls (RBAC) and least privilege principles to ensure users only have access to data and can perform actions within the scope of their roles. This limits the risk of accidental data exposure, deletion, and cyberattacks.
  8. Create audit trails of user activity around data and maintain version histories to prevent and track data loss.
  9. Follow the 3-2-1 backup rule: keep three copies of your data, store two on different media, and one offsite.
  10. Leverage the full suite of Microsoft 365 tools to monitor sensitive data, detect real-time threats, and secure information effectively.
  11. Promptly resolve detected risks to mitigate attacks early.
  12. Ensure data protection and classification policies do not impede collaboration to prevent teams from creating shadow data, which puts your organization at risk of data breaches.

For example, consider #3. If a disgruntled employee starts transferring sensitive intellectual property to external devices in preparation for a ransomware attack, having the right data use policies in place will allow your organization to stop the threat before it escalates. 

Microsoft 365 Data Protection and Classification Limitations

Despite Microsoft 365’s array of tools, there are some key gaps. AI/ML-powered data security posture management (DSPM) and data detection and response (DDR) solutions fill these easily.

The top limitations of Microsoft 365 data protection and classification are the following:

  • Limitations Handling Large Volumes of Unstructured Data: Purview struggles to automatically classify and apply sensitivity labels to diverse and vast datasets, particularly in Azure services or non-Microsoft clouds. 
  • Contextless Data Classification: Without considering context, Microsoft Purview’s MPIP can lead to false positives (over-labeling non-sensitive data) or false negatives (missing sensitive data). 
  • Inconsistent Labeling Across Providers: Microsoft tools are limited to its ecosystem, making it difficult for enterprises using multi-cloud environments to enforce consistent organization-wide labeling.
  • Minimal Threat Response Capabilities: Microsoft Defender relies heavily on IT teams for remediation and lacks robust autonomous responses.
  • Sporadic Interruption of User Activity: Inaccurate DLP classifications can disrupt legitimate data transfers in collaboration channels, frustrating employees and increasing the risk of shadow IT workarounds.

Sentra Fills the Gap: Protection Measures to Address Microsoft 365 Data Risks

Today’s businesses must get ahead of data risks by instituting Microsoft 365 data protection and classification best practices such as least privilege access and encryption. Otherwise, they risk data exposure, damaging cyberattacks, and hefty compliance fines. However, implementing these best practices depends on accurate and context-sensitive data classification in Microsoft 365. 

Sentra’s Cloud-native Data Security Platform enables secure collaboration and file sharing across all Microsoft 365 services including SharePoint, OneDrive, Teams, OneNote, Office, Word, Excel, and more. Sentra provides data access governance, shadow data detection, and privacy audit automation for M365 data. It also evaluates risks and alerts for policy or regulatory violations.

Specifically, Sentra complements Purview in the following ways:

  1. Sentra Data Detection & Response (DDR): Continuously monitors for threats such as data exfiltration, weakening of data security posture, and other suspicious activities in real time. While Purview Insider Risk Management focuses on M365 applications, Sentra DDR extends these capabilities to Azure and non-Microsoft applications.
  2. Data Perimeter Protection: Sentra automatically detects and identifies an organization’s data perimeters across M365, Azure, and non-Microsoft clouds. It alerts “organizations when sensitive data leaves its boundaries, regardless of how it is copied or exported.
  3. Shadow Data Reduction: Using context-based analysis powered by Sentra’s DataTreks™, the platform identifies unnecessary shadow data, reducing the attack surface and improving data governance.
  4. Training Data Monitoring: Sentra monitors training datasets continuously, identifying privacy violations of sensitive PII or real-time threats like training data poisoning or suspicious access.
  5. Data Access Governance: Sentra adds to Purview’s data catalog by including metadata on users and applications with data access permissions, ensuring better governance.
  6. Automated Privacy Assessments: Sentra automates privacy evaluations aligned with frameworks like GDPR and CCPA, seamlessly integrating them into Purview’s data catalog.
  7. Rich Contextual Insights: Sentra delivers detailed data context to understand usage, sensitivity, movement, and unique data types. These insights enable precise risk evaluation, threat prioritization, and remediation, and they can be consumed via an API by DLP systems, SIEMs, and other tools.

By addressing these gaps, Sentra empowers organizations to enhance their Microsoft 365 data protection and classification strategies. Request a demo to experience Sentra’s innovative solutions firsthand.

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Team Sentra
December 26, 2024
5
Min Read
Data Security

Create an Effective RFP for a Data Security Platform & DSPM

Create an Effective RFP for a Data Security Platform & DSPM

This RFP Guide is designed to help organizations create their own RFP for selection of Cloud-native Data Security Platform (DSP) & Data Security Posture Management (DSPM) solutions. The purpose is to identify key essential requirements  that will enable effective discovery, classification, and protection of sensitive data across complex environments, including in public cloud infrastructures and in on-premises environments.

Instructions for Vendors

Each section provides essential and recommended requirements to achieve a best practice capability. These have been accumulated over dozens of customer implementations.  Customers may also wish to include their own unique requirements specific to their industry or data environment.

1. Data Discovery & Classification

Requirement Details
Shadow Data Detection Can the solution discover and identify shadow data across any data environment (IaaS, PaaS, SaaS, OnPrem)?
Sensitive Data Classification Can the solution accurately classify sensitive data, including PII, financial data, and healthcare data?
Efficient Scanning Does the solution support smart sampling of large file shares and data lakes to reduce and optimize the cost of scanning, yet provide full scan coverage in less time and lower cloud compute costs?
AI-based Classification Does the solution leverage AI/ML to classify data in unstructured documents and stores (Google Drive, OneDrive, SharePoint, etc) and achieve more than 95% accuracy?
Data Context Can the solution discern and ‘learn’ the business purpose (employee data, customer data, identifiable data subjects, legal data, synthetic data, etc.) of data elements and tag them accordingly?
Data Store Compatibility Which data stores (e.g., AWS S3, Google Cloud Storage, Azure SQL, Snowflake data warehouse, On Premises file shares, etc.) does the solution support for discovery?
Autonomous Discovery Can the solution discover sensitive data automatically and continuously, ensuring up to date awareness of data presence?
Data Perimeters Monitoring Can the solution track data movement between storage solutions and detect risky and non-compliant data transfers and data sprawl?

2. Data Access Governance

Requirement Details
Access Controls Does the solution map access of users and non-human identities to data based on sensitivity and sensitive information types?
Location Independent Control Does the solution help organizations apply least privilege access regardless of data location or movement?
Identity Activity Monitoring Does the solution identify over-provisioned, unused or abandoned identities (users, keys, secrets) that create unnecessary exposures?
Data Access Catalog Does the solution provide an intuitive map of identities, their access entitlements (read/write permissions), and the sensitive data they can access?
Integration with IAM Providers Does the solution integrate with existing Identity and Access Management (IAM) systems?

3. Posture, Risk Assessment & Threat Monitoring

Requirement Details
Risk Assessment Can the solution assess data security risks and assign risk scores based on data exposure and data sensitivity?
Compliance Frameworks Does the solution support compliance with regulatory requirements such as GDPR, CCPA, and HIPAA?
Similar Data Detection Does the solution identify data that has been copied, moved, transformed or otherwise modified that may disguise its sensitivity or lessen its security posture?
Automated Alerts Does the solution provide automated alerts for policy violations and potential data breaches?
Data Loss Prevention (DLP) Does the solution include DLP features to prevent unauthorized data exfiltration?
3rd Party Data Loss Prevention (DLP) Does the solution integrate with 3rd party DLP solutions?
User Behavior Monitoring Does the solution track and analyze user behaviors to identify potential insider threats or malicious activity?
Anomaly Detection Does the solution establish a baseline and use machine learning or AI to detect anomalies in data access or movement?

4. Incident Response & Remediation

Requirement Details
Incident Management Can the solution provide detailed reports, alert details, and activity/change history logs for incident investigation?
Automated Response Does the solution support automated incident response, such as blocking malicious users or stopping unauthorized data flows (via API integration to native cloud tools or other)?
Forensic Capabilities Can the solution facilitate forensic investigation, such as data access trails and root cause analysis?
Integration with SIEM Can the solution integrate with existing Security Information and Event Management (SIEM) or other analysis systems?

5. Infrastructure & Deployment

Requirement Details
Deployment Models Does the solution support flexible deployment models (on-premise, cloud, hybrid)? Is the solution agentless?
Cloud Native Does the solution keep all data in the customer’s environment, performing classification via serverless functions? (ie. no data is ever removed from customer environment - only metadata)
Scalability Can the solution scale to meet the demands of large enterprises with multi-petabyte data volumes?
Performance Impact Does the solution work asynchronously without performance impact on the data production environment?
Multi-Cloud Support Does the solution provide unified visibility and management across multiple cloud providers and hybrid environments?

6. Operations & Support

Requirement Details
Onboarding Does the solution vendor assist customers with onboarding? Does this include assistance with customization of policies, classifiers, or other settings?
24/7 Support Does the vendor provide 24/7 support for addressing urgent security issues?
Training & Documentation Does the vendor provide training and detailed documentation for implementation and operation?
Managed Services Does the vendor (or its partners) offer managed services for organizations without dedicated security teams?
Integration with Security Tools Can the solution integrate with existing security tools, such as firewalls, DLP systems, and endpoint protection systems?

7. Pricing & Licensing

Requirement Details
Pricing Model What is the pricing structure (e.g., per user, per GB, per endpoint)?
Licensing What licensing options are available (e.g., subscription, perpetual)?
Additional Costs Are there additional costs for support, maintenance, or feature upgrades?

Conclusion

This RFP template is designed to facilitate a structured and efficient evaluation of DSP and DSPM solutions. Vendors are encouraged to provide comprehensive and transparent responses to ensure an accurate assessment of their solution’s capabilities.

Sentra’s cloud-native design combines powerful Data Discovery and Classification, DSPM, DAG, and DDR capabilities into a complete Data Security Platform (DSP). With this, Sentra customers achieve enterprise-scale data protection and do so very efficiently - without creating undue burdens on the personnel who must manage it.

To learn more about Sentra’s DSP, request a demo here and choose a time for a meeting with our data security experts. You can also choose to download the RFP as a pdf.

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Gilad Golani
December 16, 2024
4
Min Read
Data Security

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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