Traditionally, eDiscovery has been primarily associated with legal proceedings. However, as the role of data and the regulations surrounding data storage and use have evolved, the use of eDiscovery has also expanded. eDiscovery tools and processes can play a pivotal role in the data lifecycle by enabling organizations to locate and access quality data swiftly.

Consider the following eDiscovery practices that support powerful data governance.

Ensure Effective Data Identification and Classification

Data involved in eDiscovery must be searchable and accessible. Additionally, a clear chain of custody for all data ensures the preservation of data integrity, a fundamental goal in eDiscovery.

Achieving these goals requires proper indexing and classification of data through effective metadata management. This in turn makes it easier to manage the data lifecycle and retrieve relevant information when needed.

Today’s platforms offer many different locations and service providers to store information, including localized to computers, internal computer networks, and the cloud. Understanding and controlling the location of information by type is critical to avoid deduplication of effort and having conflicting or out of date documents and information. Data classification is nearly as important as where data is stored.

Prioritize Data Security and Confidentiality

To ensure regulatory compliance and prevent spoliation of data for litigation purposes, companies must focus on data security and privacy. Best practices include encrypting sensitive data and implementing access controls. Organizations should also regularly audit security protocols to prevent data breaches.

Data security and privacy also prove essential for effective data governance. Consequently, this focus on protecting sensitive data and controlling data access accomplishes two critical goals at once.

Automate Where Possible

As the data environment grows increasingly complex, legal teams can find themselves bogged down with identifying and collecting data. Automating many routine and time-consuming tasks frees up legal counsel to focus on doing what they do best. It also allows teams to establish case strategy more quickly while improving accuracy.

For example, automation streamlines tasks such as data identification and classification, deduplication, redaction, review, and analysis. This saves money in eDiscovery while simplifying data governance.

Understand the Benefits and Dangers Posed by AI

AI figures prominently in automating data tasks. It also improves speed and accuracy by quickly combing through huge datasets to identify certain types of information such as account numbers or protected health information. And by identifying trends and anomalies that human reviewers might miss, it helps to uncover hidden insights.

At the same time, however, AI introduces several significant risks. For instance, the accuracy of data used to train AI systems will affect the quality of the data classification the system produces. AI may also introduce unintended bias and privacy concerns. Consequently, while AI may prove essential, business leaders must understand the risks and use the technology wisely.

Leverage eDiscovery Technology

Utilizing eDiscovery tools not only streamlines the eDiscovery process but also reinforces data governance. For example, cloud-based and SaaS eDiscovery solutions provide increased accessibility, scalability, and flexibility. Also, by design, they handle vast amounts of data more efficiently and facilitate seamless collaboration across multiple teams and locations.

When choosing technology to support eDiscovery and data governance, organizations should look for solutions that provide robust security while scaling to handle large and complex data volumes. And to ensure these solutions deliver value, choose tools that are intuitive, easy to learn and use.

Choose Your eDiscovery Vendor Carefully

Finally, as both the data environment and the legal landscape continue to evolve, organizations will benefit from partnering with an experienced eDiscovery vendor. But take time to vet potential vendors carefully, as the decision can significantly impact both legal outcomes and data quality.

Ask detailed questions. For instance, you should prioritize vendors that can demonstrate specific experience in your industry. Also look for providers that have implemented stringent security measures to protect sensitive data and that can ensure compliance with applicable regulations.

Implement eDiscovery Best Practices for Effective Data Governance

eGovernance provides cloud-based solutions for preserving, discovering and accessing digital data within your email and document storage systems for compliance, audit, security, eDiscovery and warehousing of critical or older data. We provide fully managed solutions with access to subject matter experts in the fields of Information Governance, records retention policies and eDiscovery.

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Moving from Traditional eDiscovery to TAR

Traditional eDiscovery methods involved using keyword searches to reduce the number of documents to collect for review. In theory, this approach had merit. However, it proved both cumbersome and time-consuming.

In the first place, large volumes of data in various locations and formats make it difficult to access relevant information. Secondly, because keyword searches do not take context into account, they may miss important data.

Enter technology-assisted review (TAR), also known as predictive coding. Using AI algorithms, TAR analyzes document sets, identifying and tagging potentially relevant documents. Humans train algorithms using examples of relevant and non-relevant data. During this training phase, the algorithm learns to make educated guesses about the remaining data.

While courts have approved the use of TAR for several years, recent advances in AI promise even greater benefits.

AI Delivers Clear Benefits for eDiscovery

AI, including TAR and additional emerging tools, has the power to revolutionize eDiscovery through automating tedious tasks and offering important insights. Advantages of this technology include:

• Enhanced efficiency – By automating time-consuming tasks such as data tagging, AI can free up valuable human resources. And by prioritizing data with a higher probability of relevance, AI greatly reduces the amount of data for review. AI can also assist with creating summaries and chronologies.
• Greater accuracy – Generative AI excels at combing through large datasets to extract and highlight relevant information. AI can also assist with automatically detecting and redacting sensitive data such as PII, thus improving compliance with data privacy regulations.
• Ability to uncover hidden insights – By identifying patterns, trends, and anomalies, AI helps to reveal hidden connections that human reviewers might miss. It can also suggest documents similar to those already identified as relevant, potentially uncovering important information.
• Improved reliability, reduced bias – AI applies a consistent approach to document review throughout the process. This helps to ensure fairness and reduce inconsistencies.

Every Lucky Penny Has a Flip Side

However, with great power comes increased risk, and AI introduces several key challenges that legal teams cannot afford to overlook.

• Data security and privacy – Integrating AI into eDiscovery workflows adds another element to an already complex cyber security and privacy landscape. For instance, LLMs typically reside in cloud services such as Microsoft Azure. Additionally, threat actors can potentially poison the data used to train AI algorithms, significantly affecting output.
• Unintended bias – The accuracy of AI algorithms depends on the data used for training. An AI model trained on biased data will amplify those biases.
• Unexpected output and lack of transparency – No AI application can guarantee 100 percent accuracy, and many AI models operate as black boxes. This means that it can prove difficult to interpret how the AI tool reaches decisions. However, ethical decision making and regulatory compliance require transparency.
• Validating evidence in an era of generative AI – In addition to risks in the eDiscovery process, AI also introduces difficulties in determining the veracity of evidence. Legal teams need to ensure that they are not including deepfakes in submitted evidence.

Responsible Implementation of AI in eDiscovery

Despite these challenges, careful implementation of AI holds immense potential to continue transforming eDiscovery. Legal teams should critically evaluate AI solutions, ensure human oversight, and address ethical considerations throughout the process. With eGovernance, organizations gain access to powerful, proven AI-powered eDiscovery.

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