In a recent post, we emphasized the growing threat to PII (Personally Identifiable Information). Mainly, we are referring to the information we would once have passed only to our bankers and lawyers, but are currently providing to our favorite social media apps, preferred retailers, and other service providers.
Slack, provider of a cloud team collaboration and communication service, is the latest casualty of an external attack, resulting in unauthorized access to a user database containing user names, email addresses, and one-way encrypted passwords, phone numbers and Skype ID’s. The company reacted to the attack by launching Two-Factor-Authentication and Kill-Password functions. Up-to-date, Slack is continuing to investigate the 4-day February breach.
From an attacker’s point of view, hacking Slack’s internal customer workspace could provide valuable potential data for future operations. Retrieving identifiable credentials and other personal information regarding a large number of employees of an organization, let alone, access to their other cloud-based sensitive data (i.e. Dropbox, Github etc.) is an invaluable boost. Subsequently, an important fact Slack chose to share was that it stores “hashed” user passwords, making the above possible only if attackers succeed at decrypting the stolen passwords.
Still, this potential outcome should not be ignored. Interconnected services have not only become an essential part of the workspace, but also a potential provider of irreplaceable insider intelligence for a sophisticated attacker. Whereas a compromised personal identity matter could be addressed with a simple credit card block, compromised employee credentials at a large organization could pave the way to a devastating data breach.
Safeguarding prized user credentials scattered through multiple services is now possible through the force of machine-learning algorithms and robust anomaly detection. Deploying a set of analytical, statistical and mathematical engines on any user correlated dataset helps identifying compromised credentials through the haze of corporate big data in near real-time. Read mote Here.