Big Data Security Guide: Tools and Best Practices

Big Data Security Guide: Tools and Best Practices

Do you have a plan to manage the security risks inherent in big data sets? Are you looking for advice on best practices to secure your big data environment?

In 2023, over 343 billion credentials were leaked due to malware.

Stolen credentials are the method of choice for threat actors to gain initial access to their targets because they bypass most enterprises' security controls.

In fact, according to Verizon, 86% of data breaches involve stolen credentials.

Considering these statistics, it’s clear that securing your big data environment is more crucial than ever.

In this post you’ll learn the ten best practices and technologies you need to implement if you handle big data.

Table of contents:

What is big data security?

Big data security is a set of security measures, tools, and practices that are used to protect large data sets from unauthorized access, cyberattacks, data breaches, and other security threats. With the exponential growth of data generation and the increasing adoption of big data analytics, ensuring the security and privacy of vast amounts of data has become a critical concern for many organizations.

Benefits of big data security

Big data security enables organizations to leverage large data sets while mitigating the associated risks. Some of the main benefits include:

  1. Data protection: By implementing strong security controls, organizations can protect their big data assets from unauthorized access, theft, or misuse. This helps safeguard sensitive information, such as personal data, intellectual property, and trade secrets, from potential breaches or cyber-attacks.
  2. Compliance: Many industries are subject to data protection regulations, like GDPR, HIPAA, or PCI-DSS. Big data security controls help ensure compliance with these regulations, avoiding fines and legal penalties for non-compliance.
  3. Improved data integrity: Big data security measures, such as access controls, encryption, and auditing, help maintain the integrity and accuracy of data throughout its lifecycle. This ensures that the insights derived from big data analytics are reliable and trustworthy.
  4. Enhanced customer trust: By protecting sensitive information and ensuring privacy, organizations can build and maintain customer trust. This is particularly important in industries that handle sensitive customer data, such as finance, healthcare, or e-commerce.
  5. Risk mitigation: Big data security controls help organizations identify and mitigate potential risks associated with data breaches, cyber-attacks, or non-compliance. This protects organizations from financial losses, reputational damage, and operational disruptions.

Common big data security challenges

Securing big data is certainly a challenge as the level of sophistication attackers use has increased. Some of the major challenges to be aware of are:

  1. Data volume and velocity: The sheer volume and velocity of data generated and processed in big data environments make it difficult to secure and monitor effectively. Traditional security measures may not be scalable or efficient enough to handle the massive amounts of data involved.
  2. Data variety and complexity: Big data encompasses a wide variety of structured, unstructured, and semi-structured data types from various sources, making it challenging to apply consistent security controls across all data formats and sources.
  3. Data governance and access controls: With numerous users and systems accessing and processing big data, implementing effective access controls, authentication, and authorization mechanisms becomes increasingly complex, increasing the risk of unauthorized access or data misuse.
  4. Distributed data processing: Big data processing often involves distributed computing frameworks, like Hadoop or Spark, where data is processed across multiple nodes. Securing these distributed environments and ensuring data protection across all nodes is a significant challenge.
  5. Real-time processing and analytics: Big data environments frequently involve real-time data processing and analytics, which requires security controls that can keep pace with the speed of data ingestion and analysis without introducing significant latency or performance issues.
  6. Data encryption challenges: Encrypting large volumes of data can be computationally intensive and may impact performance, making it difficult to strike a balance between security and performance in big data environments.
  7. Insider threats: With numerous users and privileged accounts accessing big data systems, insider threats become a significant concern, requiring complex access controls, monitoring, and auditing mechanisms.
  8. Fake data generation: Fake data can be used to manipulate big data systems, leading to flawed analytics and bad decision-making. For example, fake product reviews manipulate purchasing decisions. Another example is fake news or social media posts driving down stock prices.

Big Data Security Technology Examples

While the technologies and solutions organizations choose to implement will depend on their specific requirements, here’s a list of common solutions used to help secure big data:

  1. Centralized Key Management Systems (KMS): These systems provide secure management, storage, and distribution of encryption keys used to protect data at rest and in transit. Examples include AWS KMS, Azure Key Vault, and HashiCorp Vault.
  2. Data Masking and Anonymization Tools: These tools help obfuscate or de-identify sensitive data elements, such as personally identifiable information (PII), to protect privacy and comply with data protection regulations. Examples include IBM Data Masking, Oracle Data Masking, and Informatica Dynamic Data Masking.
  3. Data Governance and Lineage Solutions: These solutions help organizations manage and enforce data governance policies, track data lineage, and maintain data integrity throughout the big data lifecycle. Examples include Collibra, Alation, and Informatica Enterprise Data Catalog.
  4. Secure Enclaves and Trusted Execution Environments (TEEs): These technologies provide isolated, hardware-based environments for securely processing and analyzing sensitive data, even in untrusted environments. Examples include Intel Software Guard Extensions (SGX), AMD Secure Encrypted Virtualization (SEV), and ARM TrustZone.
  5. Centralized Key Management: This involves managing encryption keys from a central location, ensuring that they are securely stored, rotated, and accessed only by authorized entities. Examples include HashiCorp Vault and AWS Key Management Service.
  6. Big Data Security Analytics and Monitoring Tools: These tools help organizations monitor and analyze security events, user activities, and potential threats within big data environments. Examples include Cloudera Data Platform Security, Apache Ranger, and Apache Atlas.
  7. Secure Hadoop Distributions: These distributions provide enhanced security features and controls for Apache Hadoop and related big data frameworks. Examples include Cloudera Data Platform, Hortonworks Data Platform (HDP), and IBM BigInsights.
  8. Behavioral Analytics and Anomaly Detection Tools: These tools analyze user and system behavior to detect unusual patterns that could indicate a security threat or breach. Examples include Exabeam and Darktrace.
  9. Data Encryption and Tokenization Solutions: These solutions provide strong encryption and tokenization capabilities to protect sensitive data within big data environments. Examples include Vormetric Data Security Platform, Protegrity Data Protection Platform, and OpenText Callsign.
  10. Access Management and Identity Solutions: These solutions help organizations manage and control access to big data resources, enforce role-based access controls, and govern user identities and privileges. Examples include Okta, Ping Identity, and SailPoint IdentityIQ.

Top ten big data security best practices

Implementing robust security measures is crucial when dealing with big data. Here are the top ten security practices you need to implement:

  1. Data Encryption: Encrypt data both at rest and in transit to protect it from unauthorized access and interception. This is crucial for sensitive information like personal data, financial records, and intellectual property.
  2. Access Controls: Follow the principle of least privilege, granting users and systems only the minimum level of access required to perform their tasks. Implement role-based access controls (RBAC), require multi-factor authentication (MFA), and password management via a password manager.
  3. Data Masking and Anonymization: Mask or anonymize sensitive data elements, such as personally identifiable information (PII), especially when sharing data with third parties or using it for analytics and testing.
  4. Regular Audits and Monitoring: Regularly assess the security posture of your big data environment via regular security audits, penetration testing, and red-team engagements to identify and address potential security issues.
  5. Secure Data Storage Infrastructure: Ensure that the storage solutions used for big data are secure and hardened. This includes secure configurations, patch management, and protection against physical threats.
  6. Data Governance and Classification: Establish policies and processes for managing data throughout its lifecycle, including secure data collection, storage, processing, and disposal when no longer needed.
  7. Security Training and Awareness: Educate employees, contractors, and other stakeholders on big data security best practices, threats, and their roles in maintaining data security and privacy.
  8. Incident Response and Disaster Recovery: Develop and maintain an incident response plan to handle security breaches and a disaster recovery plan to ensure business continuity in case of data loss or system failure.
  9. Data Governance: Establish clear policies, procedures, and controls for managing data access, ensuring that only authorized individuals and systems can access and process sensitive data.
  10. Security Monitoring: Monitor network traffic, user activities, and the dark web for signs of malicious activity. This includes detecting unusual patterns in network traffic, monitoring for unauthorized login attempts or data access, and scanning the dark web for any leaked or stolen data related to your organization.

Does your security team need visibility into your organization’s leaked data before it gets exploited? Book a demo to see how Breachsense can help.

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