Protecting Your Data Lake from Internal and External Threats: Security Strategies

Data lakes and lakehouses can store and analyze large amounts of both structured and unstructured data. Their ability to store diverse data sets, from customer records to operational logs, makes them powerful tools for analytics and machine learning. However, having all this data in one place also makes them valuable targets for both internal and external threat actors.

Insiders with excessive or unintended access could expose sensitive data, either through negligence or malicious intent. Meanwhile, external attackers see data lakes as high-value assets, targeting them with ransomware, data breaches, and other cyberthreats. Without proper safeguards, organizations risk data loss, compliance violations, and reputational damage. A strong security strategy is essential to ensure that data lakes remain a trusted foundation for business insights rather than a point of vulnerability.

In this article, you'll learn about some common risks that your data lake infrastructure might face, what strategies you can employ to overcome or mitigate those risks, and how Onehouse can help you achieve your secure data lake strategy.

Challenges in Securing Data Lakes

With their ability to store and process huge amounts of data from multiple sources, data lakes play an important role in modern analytics and business intelligence. However, their broad accessibility and integration with various tools introduce unique security challenges. Threats can stem from within the organization, originate from external attackers, or arise from governance issues related to managing data across multiple processing locations or sites.

Internal Risks

Not all security threats come from outside of an organization. People on the inside can also pose a threat to the confidentiality of your data. These insiders are not necessarily employees, either; they can also be contractors or partners who also have access to the data.

Common risks from internal threats include, but aren't limited to:

• Excessive permissions: Giving your users access to data that they don't necessarily require access to increases the risk of accidental exposure of sensitive data. Users or contractors could also maliciously retrieve, modify, or even delete critical data. A disgruntled employee might attempt to leak or destroy data as an act of retaliation, while an insider with financial motives could sell sensitive information to competitors or cybercriminals.
• Shadow access: As your data lakes grow, it becomes difficult to track who has access to what, increasing the risk of unauthorized data exposure or access.
• Accidental data leaks: Poor access controls and lack of visibility can lead to sensitive data being shared or exposed without proper oversight.

External Risks

The vast and diverse data sets stored in data lakes make them valuable targets for attackers, as they often contain sensitive business intelligence, customer data, and proprietary information. Some key external threats include:

• Ransomware attacks: In the event that attackers have gained access to your data lake, they can encrypt the data and demand payment for its release, disrupting operations and potentially leading to data loss.
• Data breaches: These often go hand in hand with ransomware attacks. Not only can attackers hold your data for ransom, they can also threaten to release sensitive data, which might lead to compliance violations and/or reputational damage.
• Supply chain attacks: Data lakes often integrate with third-party services and cloud applications. These external services may require access to data for various reasons, such as processing large data sets for analytics, running machine learning models, or providing specialized reporting tools. They also potentially introduce a weak point for attackers to exploit.

Governance Challenges with Multiple Data Copies

As your data needs grow, you may end up with multiple copies of the same data spread across various platforms—often because different tools as well as teams require access in their preferred formats.

This duplication introduces:

• Inconsistent security policies: When multiple copies of the same dataset are distributed across different platforms or tools, it becomes harder to consistently enforce access controls and security policies. This fragmentation increases the risk of oversight or misconfiguration, especially when governance practices don’t scale with the number of data instances.
• An increased attack surface: The more copies of your data exist, the harder it becomes to track who has access to what. An attacker might not be able to directly compromise your data lake, but a subset of your data might be accessible via a less secure analytics platform.
• Compliance complexity: Regulations often require strict control over sensitive data. For example, healthcare data may be governed by the Health Insurance Portability and Accountability Act (HIPAA), which mandates secure storage and access controls to protect patient information in the US. Similarly, the General Data Protection Regulation (GDPR) imposes stringent rules on how personal data of EU citizens should be stored, accessed, and processed. When multiple copies of data exist across different platforms, ensuring compliance with these regulations becomes significantly more challenging, as each copy must be tracked and protected according to the applicable standards.

Security Strategies to Protect Data Lakes and Lakehouses

As data lakes become an increasingly important part of your organization's infrastructure, adopting a comprehensive security strategy is essential. There are a few key methods you can use to safeguard your data lake while simultaneously maintaining its functionality and performance.

Authentication and Authorization

Strong authentication and authorization mechanisms are critical for ensuring that only authorized users and applications have access to sensitive data.

Role-based access control (RBAC) can help enforce strict access policies and limit data exposure to unauthorized users. This reduces the risk of unauthorized access, whether accidental or malicious, and ensures users only access the data they need to perform their roles.

Authentication methods, such as multifactor authentication (MFA) and single sign-on (SSO), add layers of security by verifying user identities before granting access. These methods help reduce the risk of credential-based attacks, such as phishing or password leaks.

An important concept for implementing and restricting access control to data is the principle of least privilege. This principle dictates that your systems and access policies should be designed so that any user or system can only access the least amount of data that they need to fulfill their role or function. For example, following this principle, you would restrict sensitive HR data (such as social security numbers) to HR staff and make contract information only accessible to staff with managerial status.

Monitoring and Observability

Knowing what data was accessed by whom and when is an important part of your security strategy. By continuously tracking user activities, system interactions, and data access patterns, organizations can detect abnormal behavior or unauthorized access attempts in order to respond quickly to potential threats. This is where monitoring and observability come into play.

Having a comprehensive observability strategy requires you to implement a few key points:

• Centralized logging: You can enhance your observability posture by consolidating logs from all the different data lake components, applications, and third-party services into a centralized log management system. These centralized logs provide a single pane of glass for monitoring system health and detecting access anomalies. Centralized logs also enable easier identification of unusual access patterns, such as large data downloads, multiple failed login attempts, or access from unrecognized IP addresses.
• Real-time alerts and anomaly detection: Once your logs are centralized, security teams can use security information and event management (SIEM) solutions or other analytics tools to analyze these logs and automatically flag unusual activities. For example, an alert can trigger if a user suddenly accesses a large volume of sensitive data outside of normal working hours.
• Audit trail and forensics: Centralized logging also simplifies incident investigation. In the unfortunate event of a security breach or anomaly, teams can quickly retrieve comprehensive logs from all relevant systems, making it easier to pinpoint the source of the issue, understand the full impact, and respond accordingly.

A robust monitoring and observability plan makes sure that security teams are not just reactive but also proactive in identifying and mitigating risks before they become a problem.

Data Governance

Effective data governance is the foundation of a secure and compliant data access environment. It encompasses policies, procedures, and technologies that ensure the proper management of data across its entire lifecycle, from creation and storage to access and deletion. A strong data governance strategy ensures that sensitive information is handled correctly and that the right people have the right level of access at all times.

There are a few data governance concepts that will help you to understand, secure, and limit access to your sensitive data:

• Data lineage provides a comprehensive view of where data originated from, how it flowed through the system, and where it's stored or processed. This transparency helps identify potential risks and vulnerabilities, ensuring that sensitive data is not mishandled or exposed inappropriately.
• Data classification is the process of classifying data based on its sensitivity and/or importance. This classification can help you identify the proper security controls needed for a specific piece of data.
• Data retention policies ensure that data is only stored for as long as necessary, in line with legal and business requirements. Having clear retention and deletion policies helps mitigate the risks of keeping unnecessary data that could be exposed in a breach.

Backup and Disaster Recovery

Regularly backing up critical data and testing recovery procedures can help organizations quickly restore operations in case of data corruption, ransomware attacks, or other disruptions. It's also important to store backups in secure, isolated environments—whether on-premises or in the cloud—so they’re protected by independent security controls and not exposed to the same risks as the primary data lake. A well-tested disaster recovery plan ensures that recovery can happen quickly, with minimal data loss, enabling business operations to resume quickly. This involves defining Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs), conducting regular failover tests, and performing or automating backup validation to ensure that recovery procedures actually work when needed. By planning for specific failure scenarios and continuously refining the response playbook, organizations can reduce downtime and maintain data availability during disruptions.

Minimizing Data Duplication and Ensuring Interoperability

One of the key challenges with traditional data lakes is managing multiple copies of data across different systems and platforms. By minimizing data duplication and creating a single, unified version of the data, organizations can reduce security risks and simplify governance. Opting for open table formats such as Apache Hudi^TM ensures that data remains accessible and compatible across different analytics engines without creating silos or unnecessary copies.

This approach also ties into Onehouse's unified data interoperability model, which enables seamless integration across multiple systems without the need for redundant data copies. By centralizing data in a single, interoperable environment, organizations can ensure better security, compliance, and operational efficiency. Onehouse's architecture is both secure and flexible, providing a Universal Data Lakehouse installed directly in an organization’s virtual private cloud (VPC) that ensures consistency and reduces the complexity of managing data across disparate systems.

Data Encryption

Encryption is one of the most effective ways to protect data in a lakehouse environment, both at rest and in transit. It ensures that data remains unreadable without the appropriate decryption keys even if it's exposed through unauthorized access or a system breach.

Encrypting data at rest protects it when stored on disk or in object storage, while encryption in transit safeguards data as it moves between systems, users, or applications. Together, these measures help protect sensitive information such as personal data, financial records, and intellectual property from being compromised.

To be effective, encryption must be implemented with secure key management practices. This includes rotating encryption keys regularly, restricting access to decryption credentials, and integrating with enterprise-grade key management services (KMS).

Incorporating encryption as part of a broader security strategy ensures that data is protected not just by access controls, but by strong cryptographic safeguards, adding a critical layer of defense in the event of a breach or misconfiguration.

Conclusion

Safeguarding your data lake from both internal and external threats requires a comprehensive security response. Key strategies include:

• Implementing robust authentication and authorization mechanisms
• Ensuring monitoring and observability with centralized logs
• Implementing and maintaining strong data governance policies

Adopting a single-copy data strategy offers significant advantages by minimizing data duplication across different systems. This approach simplifies governance and compliance policies while also reducing the attack surface to enhance overall security. By storing a single, interoperable version of the data, organizations can ensure seamless access, eliminate the complexities of managing multiple copies, and maintain data integrity and control across the entire organization.

Onehouse offers tools such as LakeView, Table Optimizer, and Onehouse Cloud to help you achieve a secure data access and analytics strategy for your organization.