Enhancing Privacy and Risk Management through Strategic Data at Rest Deletion
Executive Summary
In today's data-driven landscape, organizations accumulate vast amounts of information, much of which remains dormant—referred to as "data at rest." While essential for operations, this static data poses significant privacy and security risks if not managed appropriately. Organizations often overlook data at rest and its associated risks. Implementing strategic data deletion practices can bolster an organization's privacy and risk management frameworks.
Understanding Data at Rest
Data at rest encompasses all digital information stored on physical or cloud-based systems, including databases, file systems, and backups. Data at rest is a vital component of an organization’s information assets. This can include sensitive customer information, trade secrets, financial data, or any other information that is of value to the organization.
Data at rest includes both structured and unstructured data. Unlike data in transit or in use, data at rest is not actively moving through networks or being processed. Despite its static nature, it remains a prime target for unauthorized access and breaches.
Historical Context of Data at Rest
The concept of data at rest has evolved alongside technological advancements. Initially, data was stored in physical formats like paper records. With the advent of computers, data storage transitioned to digital mediums, introducing new challenges in data security and management. The increasing reliance on digital storage has amplified the importance of securing data at rest.
Data at Rest vs. Data in Transit vs. Data in Use
Data can exist in many states and can change rapidly based on business needs. Understanding the different states of data is essential for implementing appropriate security measures:
Data in transit—Data in transit refers to data actively moving from one location to another, whether across the internet, through private networks, or within a computer system. This includes information transmitted through email, collaboration platforms, instant messaging, and any other communication channel. This data is usually less secure than data at rest because it is exposed on the Internet or on a company’s private network, as it moves from one location to another.
Data at rest—Data at rest refers to information stored on a physical or digital storage medium in a non-transitory state, meaning it's not actively being moved or used. This information is stored, and is often archived, and is thus less vulnerable than data in other states. However, the information that companies store is typically very valuable to hackers, and has become a target for cyberattacks.
Data in use—Data in use is data actively being processed, accessed, or manipulated by applications or users. Data in this state is the most vulnerable—whether it is being processed, read, or modified. Granting direct access to individuals makes them vulnerable to attacks and human error, any of which can have serious consequences. Encryption is important for protecting data in use. Many companies complement encryption by adding security measures such as authentication and strict data access control.
Each state requires specific security strategies to mitigate associated risk.
Data at Rest Encryption
Encryption is the process of shuffling data so that it can only be decrypted using a key (a string of random values, which is held in confidence). Hard disk encryption is the most common way to encrypt data at rest.
Encrypting data at rest secures files and documents, ensuring that only those with the key can access them. The files are useless to anyone else. This prevents data leakage, unauthorized access, and physical theft—unless attackers manage to compromise the key management scheme and gain access to the key.
The Importance of Protecting Data at Rest
Almost every organization is at risk of a major data breach. The question is: are attackers more likely to steal data while it is stored or transmitted?
Stored data is generally considered a more attractive target for malicious hackers. It is true that data can be vulnerable at many points along its lifecycle, but modern applications typically use connections secured with the Secure Sockets Layer (SSL), an advanced encryption standard, making it difficult for attackers to listen in on communications.
When digital data is stored in a particular storage configuration for a long period of time, cyber attackers assume (mostly correctly) that it has value and would be advantageous if stolen. Indeed, data at rest is often the most sensitive data in an organization, and exposure can be devastating. Data leaks can not only cause huge losses to businesses, customers and partner organizations, but can also damage a company’s reputation and lead to regulatory fines and civil liability.
Organizational Risks Associated with Data at Rest
A critical yet often overlooked layer of protection lies in securing data at rest data stored in databases, backups, and storage devices. Storing large volumes of data at rest without proper management can expose organizations to several risks:
Unauthorized Access: Inadequate security measures can lead to unauthorized individuals accessing sensitive data.
Data Breaches: Stored data is susceptible to breaches, especially if not encrypted or properly secured.
Compliance Violations: Failure to adhere to data protection regulations can result in legal penalties.
Operational Inefficiencies: Excessive stored data can strain resources and complicate data management processes.
Incorporating Data at Rest into Lifecycle Management
Effectively managing data at rest is a critical component of comprehensive Data Lifecycle Management (DLM). DLM encompasses the entire journey of data—from its creation to its eventual disposal—ensuring that data remains secure, compliant, and valuable throughout its lifecycle. Key stages include:
Data Creation and Acquisition: The lifecycle begins with the generation or collection of data from various sources, including internal processes, customer interactions, and third-party vendors. At this stage, it's crucial to define data requirements, establish standardized formats, and implement metadata tagging to facilitate organization and retrieval. Additionally, classifying data based on sensitivity and relevance helps in applying appropriate security measures from the outset. Implementing data quality controls, such as validation checks and standardization processes, ensures accuracy and consistency in the data collected.
Secure Storage and Maintenance: Once data is acquired, it must be stored securely to protect against unauthorized access and potential breaches. Utilizing tiered storage solutions allows organizations to balance performance and cost, storing frequently accessed data ("hot" data) separately from infrequently accessed data ("cold" data). Implementing encryption, access controls, and regular audits ensures the integrity and confidentiality of data at rest. Regular maintenance practices, such as data validation and cleansing, help in maintaining data accuracy and relevance over time.
Data Usage and Processing: During its active use, data is accessed, modified, and analyzed to support business operations. It's crucial to monitor data access patterns and enforce strict access controls to prevent unauthorized usage. Employing data masking and anonymization techniques can further protect sensitive information during processing activities. Establishing clear guidelines for data access and sharing within the organization ensures that data is used appropriately and in compliance with relevant regulations.
Archiving and Retention: As data becomes less frequently accessed, it transitions into the archiving phase. Archived data should be stored securely, with appropriate indexing to facilitate retrieval if necessary. Implementing data retention policies ensures that archived data is retained for the appropriate duration, balancing regulatory requirements with storage costs. Regular reviews of archived data help in determining its ongoing relevance and compliance with retention policies.
Secure Deletion and Disposal: When data is no longer needed, it must be securely deleted to prevent unauthorized recovery. Employing methods such as cryptographic erasure, degaussing, or physical destruction ensures that data is irrecoverable. Adhering to legal requirements for data disposal, such as those outlined in GDPR, HIPAA, and CCPA, is essential to avoid legal penalties and maintain organizational reputation. Maintaining records of data deletion activities, including methods used and personnel involved, provides an audit trail for compliance purposes.
By integrating data at rest considerations into each stage of the data lifecycle, organizations can enhance data security, ensure compliance with regulations, and optimize data management practices. Implementing robust DLM strategies not only protects sensitive information but also supports operational efficiency and stakeholder trust.
Conclusion
Adopting a holistic approach to managing and securing data at rest is imperative for organizations aiming to protect sensitive information and maintain compliance. Proactive management of data at rest through strategic deletion is essential for safeguarding privacy, ensuring regulatory compliance, and mitigating risks. By integrating robust data deletion practices into privacy and risk management frameworks, organizations can enhance their security posture, ensuring resilience against evolving cyber threats and build trust with stakeholders.
