Slack Master: How to Conquer Chat eDiscovery in Three Simple Steps

Slack Master: How to Conquer Chat eDiscovery in Three Simple Steps

For a more comprehensive overview of how to handle Slack data for eDiscovery, check out Logikcull’s recent guide, “Three Simple Steps to Collect, Search and Produce Slack Data for eDiscovery

It goes over:

  • The main challenges posed by Slack discovery
  • How existing technology can help you navigate them
  • How to choose the right tool to handle Slack data
  • A three-step approach to collecting, reviewing, and producing chat data

And much more!

Download your copy now here.


With more than 1.5 billion messages generated every month and more than 600,000 organizations worldwide using Slack, the communications platform has become one of the main sources of electronically stored information (ESI) for disputes and internal investigations.

At the same time, Slack data is now one of the main headaches for even the most technically savvy IT and legal teams out there since, without the right tools and processes, conducting discovery on Slack conversations is almost impossible. 

But despite its complexity, Slack is an incredibly rich source of evidence and essential for understanding the full scope and details of your matter. If there’s one data source no legal or compliance professional should ignore, it’s Slack.

Read on to learn about some of the main challenges presented by Slack data and how they can be overcome with a simple three-step process — and the right technology.

What Makes Slack Data So Challenging?

Slack poses a noise-to-signal problem. The platform acts as a massive spiderweb that catches every employee’s stray thoughts, documents, links, gifs, videos, emojis, voice recordings, and more. 

The vast majority of that information is noise: irrelevant as evidence, but discoverable nonetheless and typically swept up in the dragnet of discovery collections. Therein lies the biggest challenge.

But it’s not just a matter of volume. The nature and novelty of this data pose a unique set of challenges for disputes and investigations. 

Here are the most common ones:

More Than Just Chat Conversations

Apart from chat messages, Slack also aggregates data from the thousands of integrations they offer — and it’s all discoverable. 

Disjointed and Unreadable Information

With Slack, you can send direct messages, chat via public or private channels, share files and other types of data through the tools it integrates with, and even edit or delete past chats. When you export all that information, it can be hard to connect all the dots. 

And then there’s this: when you export data directly from Slack, it comes out in the JSON file format, computer code, which is impossible to read unless you’re equipped with the right tool to render it.

Tricky Exports 

Slack exports come with their own set of challenges. 

For example, channels and direct messages are particularly problematic since they can’t be exported unless you’re under a Business+ plan or higher, so you would need to go through extra steps to get that information. 

Preservation and Legal Hold Challenges

Your organization must be an Enterprise Grid customer to benefit from Slack’s legal hold feature. And even then, Slack legal holds won’t:

  • Preserve messages or file data from Slack Connect spaces
  • Preserve data in shared channels 
  • Prevent users from deleting entire channels 
  • Provide preservation notices or reminders to the appropriate Slack users 

To prevent spoliation and execute a proper legal hold notice requires other tools.

Investigations Beget Investigations

Due to the nature of Slack, it’s not unusual to start reviewing Slack messages for one investigation and come out with three others.  

Digging into employee Slack data without a clear focus can yield new investigations as it’s likely you’ll uncover conversations that signal some kind of misconduct or situation that needs to be addressed separately. 

Impossible to Ignore

Unfortunately, leaving Slack data outside of the scope of the discovery process is not an option. 

Recent cases have demonstrated that courts are starting to acknowledge that discovery of Slack data is not only possible, but generally also proportional – and, thus, required.

And even if you decided to ignore Slack data during your discovery process, you’d be missing half of the story and, consequently, providing a suboptimal defense to your business or client. 

Three Steps to Conduct eDiscovery on Slack Data: Connect, Cull, and Produce

To be able to collect, search and review Slack data in a similar way to email, you need a tool that accounts for the technical challenges presented by Slack data, as well as the new communications paradigm created by chat communications. And for that, Logikcull is your best ally.

In just three simple steps, you can navigate Slack discovery seamlessly,  regardless of the size of the collection.

1. Connect (or Upload)

Collecting Slack data is usually one of the most time-consuming parts of the process for most legal and IT teams out there. With Logikcull's Slack API, it takes just a few seconds. Thanks to its integration with Slack, you’re able to pull your most relevant conversations directly from the source. 

Note that Logikcull also integrates with Google Vault, MS 365, and Box, so collecting data from those sources is equally easy.

If the Slack integration is not available to you, you can just drag and drop any previously exported files from Slack into Logikcull. The process that follows would be identical from this point on.

Upon ingestion, your Slack data goes through dozens of automatic processing steps such as deduplication, OCR’ing, conversion to readable image files, transcription of audio and video files, and, most importantly, rendering and indexing of your Slack data and metadata, so that conversations and messages can be easily filtered. 

2. Cull

You can now start further narrowing down your collection to quickly find the most responsive conversations and get any irrelevant information out of the way.

To that end, Logikcull’s filter carousel comes in handy. It operates similarly to the filters you’d use at Amazon to find a pair of shoes — except that instead of “size” or “model,” you’ll be able to filter by criteria relevant to Slack, such as “channel,” “participants” and “reactions.” 

Look at the GIF more closely. After applying a simple “Sender” filter, the original data set is reduced by 80%.

Less Data to Review and a More Streamlined Way to Do It

After culling all irrelevant documents, you can hone in on your most responsive conversations by searching for relevant keywords and analyzing the results of your search.

Once you’re ready to conduct an eyes-on review of your most relevant conversations, you’ll have a host of review capabilities and collaboration options available that will greatly streamline your process, such as bulk redactions. 

Take a peek:

3. Produce

Once the review of your Slack data is completed, you’ll be able to either generate a production in any format required by the opposing party — or you can just download all conversations as a single PDF with just a click.

You may also share a safe link with the other party so they can access your reviewed documents directly in Logikcull, which enables you to remain in control of your data up to the very last step of the process.

In a nutshell, Logikcull allows you to keep the entire discovery process in a single secure place and streamline your Slack data management every step of the way — from preservation to production.


If you’re interested in testing this streamlined process with your own Slack data, feel free to book a demo with us or start a free trial of Logikcull.

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