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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Improved Analytics and Automation Strengthen Compliance Efforts

Through analytics and automation, AI proves a game-changer for regulatory compliance. For instance, in a constantly evolving compliance landscape, organizations struggle to keep track of regulatory changes.

AI-enhanced tools bring the ability to analyze large and diverse data sets such as industry regulations, privacy laws, internal policies, and contracts. Using natural language understanding and semantic analysis, AI can help organizations identify relevant regulations and map their internal policies and procedures accordingly.

Additionally, AI allows the automation of tedious tasks such as data collection and classification, reporting, and compliance monitoring. Automating these tasks improves both speed and accuracy. It also helps compliance officers find and track data on multiple platforms throughout the organization.

Unique Risks Necessitate Updated Strategies

However, AI also poses new compliance challenges that require a proactive approach. In the first place, AI and machine learning rely on large amounts of training data. To avoid bias and ensure effective decision-making, companies must ensure these systems have access to accurate, complete, and relevant information.

At the same time, the data used for AI training models may contain sensitive or personal information. Organizations need to adjust policies and procedures around data collection, storage, and use to make sure sensitive data remains compliant.

The rise of AI also introduces new cyber security risks. AI-powered cyber-attacks greatly increase the risk of successful phishing attempts and data breaches. To demonstrate compliance, companies must be able to show that they have implemented appropriate security measures to counter these evolving threats.

Finally, compliance requires transparency and accountability. While automation delivers key benefits, it requires human oversight. Organizations will need to disclose their use of AI systems. They must also be able to explain the logic, methods, and limitations of their AI systems to outside regulators.

Best Practices for Effectively Managing AI and Compliance

To leverage the benefits of AI while providing data security and transparency, businesses should adopt a holistic approach. For example, they should:

• Strengthen information governance with AI in mind – Effective AI depends on huge amounts of high-quality data. More than ever, businesses must know where their data lives and what their data contains. And they must carefully control AI access to data based on sensitivity and regulatory requirements.
• Adjust cyber security policies and procedures as necessary – Cyber security strategies need to account for emerging AI-powered cyber threats such as deepfakes. Additionally, security teams must guard against threat actors that attempt to manipulate AI algorithms and corrupt the data that feed them.

• Implement regular audits and compliance monitoring – Regularly audit AI systems for potential vulnerabilities. This may include adjusting algorithms to accommodate regulatory requirements. Continuous compliance monitoring should include policies specific to data usage for AI.
• Provide robust training – Businesses should provide effective training to staff on the principles, practices, and implications of AI projects. For instance, employees need to understand the potential for bias and other ethical issues. And they need to understand how to recognize and remedy potential problems with AI systems.

Build a Comprehensive and Agile Data Compliance Strategy

AI introduces new vistas in a constantly changing data environment. For compliance, it presents a double-edged sword. On the one hand, AI can streamline compliance efforts and allow organizations to effectively manage and monitor data at scale. On the other hand, AI introduces increased security risks and complicates compliance efforts.

eGovernance compliance solutions provide critical data visibility and control throughout the organization. They simplify data classification and compliance monitoring and provide critical transparency. Combined with comprehensive data security from eMazzanti, eGovernance delivers the peace of mind businesses need.

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Managing Data in Diverse Locations and Formats

The increasing adoption of cloud services, mobile devices, social media, and the IoT places substantial demands on eDiscovery systems. Data generated and stored in numerous locations and formats proves difficult to find and manage. Remote work further complicates the process by increasing the risk of data loss due to the use of personal devices and insecure networks.

For instance, data relevant to a given case may appear in videos, documents, text messages, and security camera footage. Information that lives on mobile devices or in highly disorganized data stores in legacy systems present additional challenges for legal teams.

To address these challenges, organizations should take a proactive and holistic approach to information governance. This involves implementing policies and procedures to track and secure data throughout its lifecycle. But classifying and monitoring sensitive information at scale, as well as enforcing security and privacy policies, requires automation.

AI Plays Complex Role Technology for eDiscovery

AI has become essential to automating and optimizing information governance tasks such as data classification and policy enforcement. It can also help legal teams improve the accuracy and efficiency of eDiscovery outcomes by automating data deduplication, redaction, document review, and predictive coding.

Further, AI enables more advanced capabilities like sentiment analysis and topic modeling. These capabilities help legal teams understand the context and meaning of the data, as well as identify relevant facts and patterns. Note that AI does not replace information governance. But it can help information governance personnel find and process data at scale.

On the other hand, AI adds risk to information governance and eDiscovery processes. For example, using poor quality or insufficient data to train AI algorithms can result in biased or inaccurate results. False or misleading texts in turn affect the reliability and admissibility of evidence.

Organizations using generative AI should implement best practices including flagging text generated by AI. They should also apply rigorous quality control to ensure that generated texts are accurate, relevant, and consistent with original sources. Further, clear protocols and standards for the use and disclosure of AI in discovery will prove essential.

Cloud-based eDiscovery Delivers Scalability, Efficiency

Cloud-based solutions dominate the eDiscovery technology landscape because they offer lower costs, greater scalability, and faster deployment. Additionally, they deliver vastly improved accessibility for teams spread across distance. Consequently, experts predict that legal adoption of cloud-based technology will continue to grow rapidly in the coming year.

Cloud computing allows organizations to store, process, and access large amounts of electronically stored information (ESI) from anywhere. This improves collaboration, saves time, and promotes data security by allowing responders to perform an initial data review in place.

Blockchain on the Horizon for eDiscovery

While not yet in mainstream use, blockchain technology has the power to play an increasingly useful role in enhancing eDiscovery. By recording transactions in a secure, transparent, and immutable way, it promises significant eDiscovery benefits, including:

• Verifying the authenticity and provenance of ESI
• Tracking data ownership, custody, and transfer
• Reducing the risk of data breaches by strengthening access control and encryption
• Promoting efficiency and accuracy by enabling smart contracts and automating tasks such as data collection, preservation, and review
• Enforcing compliance with regulations such as data retention policies

Proactive legal teams will keep a close eye on developments in blockchain technology, as it could have a substantial impact on eDiscovery.

Partner with Technology for eDiscovery Experts

Navigating eDiscovery challenges and opportunities requires an approach that harnesses technology for eDiscovery while instituting information governance best practices. With eGovernance solutions for eDiscovery and information governance, organizations reduce costs while simplifying processes and producing consistent, defensible results.

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