Plurilock provides continuous, regulation-friendly multi-factor authentication—
of cybersecurity incidents
in healthcare involve insiders
When well-being is on the line, delays—even for security—aren’t acceptable.
Healthcare organizations struggle with authentication practices. Yet healthcare is also the one industry in which internal actors are the biggest threat to data security.1
Use Plurilock to safeguard personal health information (PHI) at your healthcare organization.
Advanced Protection for Healthcare Computing
How It Works
Behavioral Biometric Factors
Plurilock uses behavioral-biometric data, including keystroke patterns, mouse movement tendencies, and touchscreen behavior, to identify users. No activity or personal information is recorded or stored; instead, AI systems compare current movement patterns for a user with past ones—to catch intruders immediately.
Combined with Environment and Context Data
The analysis of movement micro-patterns is combined with additional data from environmental sources, sensors, and network traffic, to ensure not only that it’s the right user, but that they’re in the expected place. The result is bulletproof identity verification that happens transparently.
Correct user, correct login, and correct password.
Even with valid credentials, intruders are spotted and stopped.
Architecture and Deployment
Ongoing authentication checks and decisions are logged and flagged with risk and certainty levels. Administrators use a backend dashboard to manage policy, view metrics, or feed event data into SIEM systems for integration existing security infrastructure.
Why It's Privacy-safe
Conventional authenticators like SMS codes, secret identifying questions, and physical biometrics all create privacy and risk issues of their own by storing and transmitting identifying data. Plurilock, on the other hand, analyzes tiny patterns in ambient behavior, environment, and context data.
The data is numeric, constantly evolving, contains no personal information, and can’t be used to identify real individuals—even if lost or stolen.
Whether due to phishing attacks, data theft, user carelessness or other breaches, once attackers obtain legitimate credentials, they can cause limitless harm and are almost impossible to detect.
With Plurilock, every authorized user is silently matched against a known behavioral, environmental, and contextual profile. Strangers are recognized and excluded—no matter which credentials they’ve stolen.
Multi-factor authentication is there to protect sensitive data, assets, employees, and patients—not to stop care altogether. Yet too often, this is the ultimate result.
Plurilock’s advanced MFA solutions enable strong multi-factor authentication without extra hardware, login steps, interruptions, or memorization, enabling providers to get on with critical work.
More and more healthcare activity is carried out today in cloud-based applications. To stay current, providers and related organizations are struggling with the cloud, too—but concerns about privacy and data theft are real.
With Plurilock, the weakest link—online authentication prompts—recognize actual intended users, not just credentials. Everyone else blocked in real time. For in-house online applications, continuous authentication for cloud apps offers maximum protection.
When busy providers and employees secretly share licenses, logins, or passwords, they may get work done faster, but at the expense of security and significant legal risk.
Plurilock recognizes only the authorized individual—not a set of credentials—stopping account sharing dead in its tracks. But because Plurilock works invisibly, and in real time, users no longer hesitate to log into their own accounts for everyday work.
Internal bad actors, a significant risk in healthcare organizations, use legitimate employees’ credentials or workstations for criminal activity. In the world of healthcare, the results can be devastating to organizations.
Because Plurilock recognizes actual human beings and not just credentials, neither inadvertent nor malicious privilege elevations are possible, leaving insiders no way to engage in illicit activity.
Even the most conscientious users make mistakes with credentials or sessions, writing notes to remember passwords or stepping away from workstations at critical moments without first logging out.
Plurilock ensures that these momentary lapses don’t represent a catastrophic risk. Lost credentials and abandoned workstations no longer mean unguarded access. Bad actors are unable to make use of them.
Deploy advanced MFA at your healthcare organization today.
Achieve Standards Compliance Sensibly
No. Plurilock analyzes the way that they move—characteristic times and patterns between and during keystrokes and gestures—rather than what they type or do. The data is numeric, yet highly individual, and it is this data Plurilock’s tools analyze—not the content of typing or other activity.
Plurilock’s authentication technology is lightweight,
using far less than 1% of any system
resource, including memory or CPU cycles, on
any modern endpoint or computing device.
It’s easy. Our web solutions rely on HTML5+JS and our concise API. Our other solutions support common centralized admin tools.
No. Plurilock analyzes the way that computing tasks are carried out—characteristic patterns in speed, mouse acceleration,
movement angles, and actuation timings— not what users do as they interact with their mouse or touchscreen.
Profile data is encrypted for transmission and for storage in a secure central database. A key Plurilock advantage is that even if somehow lost to the wild, stolen profile data cannot be used to reconstruct a user’s real-world identity, or to impersonate the user’s identity profile in order to gain illicit access
No. Recorded activity does not have the same micro-variations over time that real humans exhibit. Recorded movements are not recognized as being authentic input from the intended user, and are detected and blocked.
When behavior changes significantly, initial authentication failures may result. Either step-up authentication or manual action is used to provide access in these cases, causing a “false positive” signal is sent to Plurilock’s machine learning algorithms, which then begin to learn the “new” interaction style. The user profile is updated accordingly, and
transparent authentication then returns.