Overview
The Challenge: LifeLabs needed visibility into user activity across applications holding sensitive patient data. Previous monitoring solutions were costly to maintain, delivered poor results, and left dangerous gaps that increased the risk of regulatory penalties, reputational damage, and costly remediation.
The Solution: Full visibility into human and non-human identity behavior across SaaS, cloud, and custom applications with accurate detection that reduces false positives and alert fatigue.
The Result: Over 50% reduction in mean time to detect and respond to threats. Detection of both known and unknown identity-based threats in applications with patient data – and a security team that actually loves using the platform.
“My team doesn’t have to be sold on it. They just love using it.”
— Mike Melo, CISO, LifeLabs
The Challenge
Dangerous Gaps in Identity and Application Security
LifeLabs, Canada’s largest medical diagnostics services provider (now part of Quest Diagnostics), manages vast amounts of sensitive patient data in both SaaS applications and a legacy custom application.
As CISO Mike Melo explains: “We knew we didn’t have good enough visibility into user activity in our applications that hold sensitive data.”
The Problem: Despite an already robust security stack, the LifeLabs team lacked post-authentication visibility – making it difficult to detect insider threats and identity-based attacks in their application environment. Previous home-grown monitoring solutions were costly to maintain and delivered poor results, leaving gaps that increased the risk of regulatory penalties, reputational damage, and costly remediation.
What Didn’t Work:
- Legacy Monitoring Solutions: Costly to maintain and delivered poor results
- Post-Authentication Blind Spots: No visibility into what identities were doing inside applications
- Limited Detection: Couldn’t catch insider threats or identity-based attacks in near real-time across critical applications
The Solution
Accurate Identity Behavior Detection Across All Applications
LifeLabs turned to Reveal Security to fill the visibility and threat detection gap in their critical applications.
What Changed: The Reveal Platform gives security teams full visibility into human and non-human identity behavior across SaaS, cloud and custom applications. It then accurately detects anomalies that indicate insider threats or account compromise – reducing false positives and alert fatigue.
Immediate Impact: “With Reveal Security, we get an extremely accurate representation of how our users and identities are interacting with our data and application systems.”
Key Results
Real Threats Stopped. Real Time Saved.
Faster Detection and Response
Reduced MTTD & MTTR by over 50%
Cut mean time to detect and mean time to respond in half, enabling faster containment of identity-based threats before they cause damage.
Accurate Detection of Insider and Identity-Based Threats
Detection of both known and unknown threats in applications with patient data
Accurately identify suspicious activity, compromised accounts, and insider threats across all critical applications holding sensitive health information.
Analysts More Engaged, Responsive, and Effective
Security team adoption and satisfaction
“My team doesn’t have to be sold on it. They just love using it.” The platform empowers analysts with clear, actionable alerts they can trust and act on immediately.
Reliable Threat Detection
“Having a solution that we can rely on during any sort of threat detection – whether it’s known or unknown – is huge for us.”
Melo notes the critical importance of having a platform that catches both familiar attack patterns and novel threats in healthcare’s high-stakes environment.
About LifeLabs
With a legacy of serving Canadians for over 50 years, LifeLabs (now part of Quest Diagnostics) is the country’s largest medical diagnostics services provider.
Operating from more than 400 locations nationwide, the company processes millions of patient interactions annually and maintains an unwavering commitment to protecting sensitive health data.