Organizations often rely on Amazon Web Services (AWS) to store sensitive data in the cloud, which requires robust security measures to protect this data from theft, loss and corruption. Data Loss Prevention (DLP) enables administrators to classify data, define access rules, and monitor how this data is being access and used. AWS offers a comprehensive suite of data protection tools that adhere to DLP standards, providing multiple layers of security. However, many companies also choose to use third-party DLP solutions for more customised control. By leveraging machine learning (ML) algorithms, DLP solutions can proactively and intelligently minimize the likelihood of data loss.
Why is AWS DLP Important for Businesses?
Data loss can be attributed to various factors, both internal and external. Negligent insiders, including employees and contractors, pose a significant threat, accounting for the majority of data breaches. Employees may inadvertently download files to vulnerable USB drives or misuse company assets for personal gain, while third-parties can also pose similar risks. Data loss exposes organizations to a range of vulnerabilities, including breaches, industrial espionage, and non-compliance with regulatory standards. A company’s reputation and competitive edge can suffer long-term consequences as a result of data loss, emphasizing the importance of rigorous data protection measures.
How Does AWS DLP Help with Compliance?
Administrators often rely on AWS DLP to assist with ensuring compliance with industry-specific regulations. Examples of these regulations include HIPAA, PCI-DSS, GDPR, FISMA, and CCPA. Failure to comply with these regulations can lead to significant fines and penalties. Before implementing storage policies, administrators should seek guidance from experts to ensure compliance. Compliance regulations cover data at rest, which is stored on AWS servers, and data in motion, which is transferred over the network or internet. Strict standards, such as encryption, must be followed for both data at rest and in motion.
AWS DLP Best Practices
To ensure data security in AWS, the best place to start would be to classify your data. Once classified, you can select the appropriate AWS architecture that offers the necessary data management and security controls to safeguard data at rest and in motion. Defining roles and authorization rules is essential to control who is authorized to access the data. Additionally, documenting every procedure ensures that all employees, including new hires, can follow specific, repeatable processes, ensuring consistency and adherence to security best practices. To safeguard sensitive data in AWS, various approaches are available, including encryption, tokenization, and access control. However, their effectiveness heavily relies on continuous security monitoring and careful configuration of security policies. Below are some of the most notable AWS DLP best practices.
1. Use AWS Encryption
Amazon S3 offers automatic encryption for data at rest with various options. With AWS Managed Keys (SSE-S3), unique keys are used for encryption along with multi-factor encryption. The encryption process is carried out server-side using 256-bit AES-256. Using AWS KMS-Managed Keys (SSE-KMS) provides an audit trail of key use and allows for keys to be generated and managed through the service. These keys can also be employed for client-side encryption. Customer-Provided Keys (SSE-C) enables users to create their own keys or use a third-party service. Amazon handles server-side encryption, but client-side encryption is typically the responsibility of the customer.
2. Carefully Monitor S3 Buckets
Amazon’s S3 “shared responsibility” model means that Amazon is responsible for the external environment while your company must focus on ensuring the security of the data within the S3 container. However, it is not just the data inside the bucket that needs to be monitored. In recent years, we’ve seen a number of high profile data breaches caused by misconfigured, or “leaky” buckets. Hence, it is crucially important to monitor both the access and usage of the data, and any changes to the configurations options that determine the security of the bucket.
3. Locate and Classify All Sensitive Data
Organizations must protect their sensitive data from evolving threats, which involves classifying data to determine appropriate security measures. Moving beyond simple public or private descriptions, data classification should include various levels of data sensitivity. Data classification can be used by tools that both detect and prevent data loss. For example:
User Behavior Analytics (UBA) solutions often use Machine Learning (ML) techniques to automatically detect suspicious activity based on your data classification schema. By integrating UBA with real-time alerting based on predefined thresholds, organizations can proactively prevent data loss.
It’s worth nothing that Amazon Macie uses advanced pattern-matching techniques to locate sensitive data, forming the foundation of a robust DLP framework. This framework comprises three main components: monitoring, policy enforcement, and automation. Monitoring involves continuous surveillance of network egress, public-facing devices, and configuration changes, raising alerts for unauthorized access or security policy deviations. Policy enforcement entails creating specific rules based on data classification, leveraging services like AWS Organizations and AWS Config to ensure compliance.
4. Swim-lane Isolation
Swim-lane isolation organizes microservices into separate domains based on your business needs, enabling distinct separation of duties between different roles. This segregation establishes a data-access structure that restricts only designated APIs to have authorization to view or manipulate data, thus preventing any data leakage from more secure domains to less secure ones. For example, a bank might have swim-lane isolation between its payment domain APIs (highly sensitive data) and marketing domain APIs (publicly available data). The implementation of swim-lane isolation involves a blend of IAM controls and ACLs tailored to the specific requirements of each domain.
How Lepide Helps to Prevent Data Loss in AWS
The Lepide Data Security Platform can help to safeguard an organization’s data from unauthorized access and breaches. Its data discovery and classification capabilities can easily locate sensitive data in unstructured data stores, as well classify sensitive data at the point of creation or modification. The platform’s access governance capabilities can highlight excessive permissions, enabling administrators to implement stringent access controls. Additionally, its real-time monitoring features empower security teams to detect anomalous user activities and identify potential threats by correlating event logs from multiple sources.
If you’d like to see how the Lepide Data Security Platform can help to prevent data loss, schedule a demo with one of our engineers.