Defend sensitive data from loss and exfiltration

Collaboration tools enable teams to work together remotely by providing features such as real-time chat, video calling, screen sharing, and more. Aware supports all leading collaboration tools include Slack, Microsoft Teams, Webex by Cisco, Zoom Team Chat, Google Drive, and Workplace from Meta. See our Integrations page for a comprehensive list. Thanks to Aware’s Context API, enterprises can also connect their own workplace collaboration solutions with Aware’s intelligent data fabric, providing insights and analysis to almost any collaboration dataset.  

By default, all major collaboration tools allow the end user (“custodian”) to retain complete control over the messages they send. That means at any point a user can edit or delete anything they’ve entered into a collaboration tool. Without a data loss prevention (DLP) solution that can capture a complete record of these conversations as they happen, that context can be lost forever. This is especially critical because collaboration tools often sync across multiple devices, including personal devices enabled under BYOD policies, and can instantly transmit large amounts of sensitive data outside the control of the organization. A DLP solution built for collaboration can mitigate this risk by capturing the context of all activities that take place within those tools. 

Aware ingests collaboration tool data in near real time and automatically runs messages through Artificial Intelligence and Machine Learning (AI/ML) infused workflows designed to detect sensitive and noncompliant data-sharing. Aware provides customizable workflows for major compliance regulations such as HIPAA and FINRA, and organizations can create their own workflows for their unique confidential and proprietary data.  

Aware uses regular expressions (regex) and Boolean logic to search for the most commonly shared confidential or sensitive data. Examples include:

Payment card industry (PCI) information such as credit card numbers, CVV codes, and bank account details

Protected health information (PHI) like diagnoses, medications, and medical appointment dates   

Personally identifiable information (PII) information including date of birth, SSN, telephone numbers and addresses

Aware also enables security teams to protect proprietary and sensitive information specific to the enterprise with customizable workflows that can search for any term or phrase. This enables information security controls over details such as mergers and acquisitions, intellectual property (IP), and other confidential business data.

Artificial Intelligence and Machine Learning (AI/ML) infused workflows are an essential component of Aware’s collaboration intelligence platform. These workflows automate the complex work of searching for, identifying, and flagging potentially sensitive, confidential, or proprietary data. With legacy data loss protection tools, infosec leaders may have to manually search through millions of messages and still not have a complete picture of all the potential risks living within collaboration tool data. AI/ML-infused workflows that run in real time give businesses proactive oversight of their collaboration data, enabling more advanced DLP strategies.  

Data loss protection encompasses many different strategies for managing how data moves throughout the organization. The goal of DLP is to secure sensitive data and protect it from being accessed or exfiltrated by unauthorized parties. While studies show that many data loss incidents and breaches are the result of employee error, insider threats remain the most costly and dangerous to modern businesses. Sentiment scoring normalized for the enterprise can help information security leaders to proactively identify where the risk of insider threat is highest by surfacing increased levels of toxicity.  

According to the Verizon Data Breach Investigations Report, negligent insiders are by far the most common insider risk to any organization, responsible for 62% of all data security incidents. Malicious insiders are responsible for just 14% of insider data breaches. The remainder of cases involved stolen credentials.   

Despite being more common, negligent insider threats are typically the least damaging to the enterprise. The average cost of remediating insider negligence was just over $300,000 in 2020, compared with over $750,000 when a malicious insider was the cause of a data breach. Malicious insiders can do more damage by deliberately targeting a company’s most valuable data and evading attempts to be identified.   

Overall, insider risk of all kinds is responsible for just 20% of data breach incidents. The other 80% involve external threat actors. However, the cost of remediating an insider breach is much higher — the average insider exfiltrates 10 times more information than the average external threat actor.

Insider risk can be hard to detect in any organization, but disgruntled employees do make themselves known through their actions. Aware built the industry’s leading natural language processing (NLP) and sentiment scoring models, built for the nuances of collaboration datasets and trained on millions of real collaboration messages. As Aware ingests collaboration data, artificial intelligence workflows analyze and score the sentiment of messages and deliver results normalized for each individual organization. That sentiment scoring provides greater insight into how the mood shifts within the organization, where negativity is strongest, and where toxicity starts to take hold. These insights enable organizations to be proactive about addressing areas with a high potential for insider risk.  

Aware supports forensic search and investigations into collaboration data for a wide range of use cases. Some of the top reasons our customers use Aware to search conversation data include:     

Compliance violations    

Acceptable Use Policy violations   

Intellectual property protection    

Data loss    

HR investigations

Collaboration tools can complicate information governance because they make it harder to preserve data. In collaboration tools like Slack and Teams, custodians or end users retain full control of the messages they send. That means a user can edit or delete a message, even months after sending, and those changes will sync across the workspace in real time. This presents challenges for administrators implementing records retention policies. One solution is to disable the ability for custodians to edit or delete their messages. However, this functionality is not available in all collaboration tools, and it can create confusion or frustration when employees make simple spelling mistakes. It may also introduce new risks if an employee accidentally shares sensitive content in the wrong location and is unable to redact it. Aware provides a better solution by capturing a complete record of all messages, including revisions and deletions, in real time. From a centralized pane of glass, Aware give information governance leaders insight into what is happening within their collaboration tools, with no loss of context.  

Aware was built to help innovative companies mitigate risk and realize the value locked within their collaboration tool data. Aware ingests collaboration messages in real time and infuses them with intelligent analysis that reveals the hidden context of how modern businesses run. From Aware’s contextual archive, businesses can access a range of use cases, including: compliance adherence, eDiscovery, employee experience, toxic hot spot detection, information governance, and more.