[ad_1]
The legal discovery process is getting easier all the time, thanks to AI. But storing all that big data requires extra thought that IT should consider.
Image: Getty Images/iStockphoto
In law, the legal discovery process is a pre-trial arrangement between opposing parties to exchange information about witnesses and evidence that will be presented at trial. During this process, each side collects information, requests answers from the other side and provides responses to questions from the opposition.
Legal discovery becomes an IT issue when it turns into e-discovery, which is the process of seeking and exchanging this information in electronic format. It becomes a big data issue because most of the documents and evidence that will be needed for e-discovery is in the form of big data, such as free-form text documents, videos and photos, diagrams, voice recordings, etc.
SEE: Electronic Data Disposal Policy (TechRepublic Premium)
Here are some primary issues that IT should consider.
How will you store the data?
Cloudnine, a legal software vendor, recently wrote about the idea of mapping e-discovery data to the various storage sources where it’s kept.
Companies might store five to seven years of corporate emails in house on a secure server and maintain paper-based documents that have been scanned on cold storage, like slow and cheap disk drives. In other cases, such as searches (and records) of social media, using a cloud-based storage option might be best.
What’s important is that IT keeps track of where all of this data is so it can be brought together during e-discovery. Cloudnine calls this “data mapping.”
SEE: Snowflake data warehouse platform: A cheat sheet (free PDF) (TechRepublic)
“Prepare a list [for legal discovery] and continue to add to it to ensure all of the types or data are considered,” said Cloudnine in a blog post. “These can include email, work product documents, voice mail, databases, web site, social media content, hard copy documents, and any other type of data in use within your organization.
“A data map is no good unless you identify the department or custodian responsible for the data. Some of these may be kept by IT (e.g., exchange servers for the entire organization) while others could be down to the individual level (e.g., access databases kept on an individual’s laptop),” the blog post said.
How long do you have to keep data?
In Washington, legal property records are required to be retained for a period of seven years, but legal data retention requirements vary by jurisdiction, so it will be up to the legal team to inform IT what document retention policies need to be, depending on which states in which the firm operates.
There are also likely to be cases where it will be unclear how long organizations need to retain data. This makes it imperative for IT to regularly meet with the legal team to review data retention policies and to notify the legal team in advance whenever a purge of e-discovery data is due.
An alternate failsafe strategy is for IT to keep all data, moving it off to cold storage.
How can lawyers search data?
Lawyers once hired clerical workers who pored through documents and coded key terms known as “entities.” The purpose was to make document searches easier. Now with e-discovery, e-searches can be done with the help of artificial intelligence. A number of vendors provide AI search engines for this purpose.
IT should work with the legal team during vendor selection to assure that a cooperative legal and IT partnership is enabled with the vendor.
Think about data security from a legal perspective
In the course of setting up an e-discovery system, IT will meet with the legal team to define security access, but there are also legal regulatory practices that must be considered.
One of these is custody. For example, if a company experiences a data breach, there is risk that data could be altered or disclosed. IT and the legal team should determine what the mitigation strategy should be if this happens.
Data, Analytics and AI Newsletter
Learn the latest news and best practices about data science, big data analytics, and artificial intelligence.
Delivered Mondays
Also see
[ad_2]
Source link
