Bottom Line: One of the primary factors motivating employees to sacrifice security for speed are the many frustrations they face, attempting to re-authenticate who they are so they can get more work done and achieve greater productivity.
How Bad Security Experiences Lead to a Breach
Every business is facing the paradox of hardening security without sacrificing users’ login and system access experiences. Zero Trust Privilege is emerging as a proven framework for thwarting privileged credential abuse by verifying who is requesting access, the context of the request, and the risk of the access environment across every threat surface an organization has.
Centrify’s recent survey Privileged Access Management In The Modern Threatscape found that 74% of data breaches start with privileged credential abuse. Forrester estimates that 80% of data breaches have a connection to compromised privileged credentials, such as passwords, tokens, keys, and certificates. On the Dark Web, privileged access credentials are a best-seller because they provide the intruder with “the keys to the kingdom.” By leveraging a “trusted” identity, a hacker can operate undetected and exfiltrate sensitive data sets without raising any red flags.
Frustrated with wasting time responding to the many account lock-outs, re-authentication procedures, and login errors outmoded Privileged Access Management (PAM) systems require, IT Help Desk teams, IT administrators, and admin users freely share privileged credentials, often resulting in them eventually being offered for sale on the Dark Web.
The Keys to the Kingdom Are In High Demand
18% of healthcare employees are willing to sell confidential data to unauthorized parties for as little as $500 to $1,000, and 24% of employees know of someone who has sold privileged credentials to outsiders, according to a recent Accenture survey. State-sponsored and organized crime organizations offer to pay bounties in bitcoin for privileged credentials for many of the world’s largest financial institutions on the Dark Web. And with the typical U.S.-based enterprise losing on average $7.91M from a breach, more than double the global average of $3.86M according to IBM’s 2018 Data Breach Study, it’s clear that improving admin user experiences to reduce the incidence of privileged credential sharing needs to happen now.
How Machine Learning Improves Admin User Experiences and Thwarts Breaches
Machine learning is making every aspect of security experiences more adaptive, taking into account the risk context of every privileged access attempt across any threat surface, anytime. Machine learning algorithms can continuously learn and generate contextual intelligence that is used to streamline verified privileged user’s access while thwarting many potential threats ― the most common of which is compromised credentials.
The following are a few of the many ways machine learning is improving privileged users’ experiences when they need to log in to secure critical infrastructure resources:
- Machine learning is making it possible to provide adaptive, personalized login experiences at scale using risk-scoring of every access attempt in real-time, all contributing to improved user experiences. Machine learning is making it possible to implement security strategies that flex or adapt to risk contexts in real-time, assessing every access attempt across every threat surface, and generating a risk score in milliseconds. Being able to respond in milliseconds, or real-time is essential for delivering excellent admin user experiences. The “never trust, always verify, enforce least privilege” approach to security is how many enterprises from a broad base of industries including leading financial services and insurance companies are protecting every threat surface from privileged access abuse. CIOs at these companies say taking a Zero Trust approach with a strong focus on Zero Trust Privilege corporate-wide is redefining the legacy approach to Privileged Access Management by delivering cloud-architected Zero Trust Privilege to secure access to infrastructure, DevOps, cloud, containers, Big Data, and other modern enterprise use cases. Taking a Zero Trust approach to security enables their departments to roll out new services across every threat surface their customers prefer to use without having to customize security strategies for each.
- Quantify, track and analyze every potential security threat and attempted breach and apply threat analytics to the aggregated data sets in real-time, thwarting data exfiltration attempts before they begin. One of the tenets or cornerstones of Zero Trust Privilege is adaptive control. Machine learning algorithms continually “learn” by constantly analyzing and looking for anomalies in users’ behavior across every threat surface, device, and login attempt. When any users’ behavior appears to be outside the threshold of constraints defined for threat analytics and risk scoring, additional authentication is immediately requested, and access denied to requested resources until an identity can be verified. Machine learning makes adaptive preventative controls possible.
- When every identity is a new security perimeter, machine learnings’ ability to provide personalization at scale for every access attempt on every threat surface is essential for enabling a company to keep growing. Businesses that are growing the fastest often face the greatest challenges when it comes to improving their privileged users’ experiences. Getting new employees productive quickly needs to be based on four foundational elements. These include verifying the identity of every admin user, knowing the context of their access request, ensuring it’s coming from a clean source, and limiting access as well as privilege. Taken together, these pillars form the foundation of a Zero Trust Privilege.
Organizations don’t have to sacrifice security for speed when they’re relying on machine learning-based approaches for improving the privileged user experience. Today, a majority of IT Help Desk teams, IT administrators, and admin users are freely sharing privileged credentials to be more productive, which often leads to breaches based on privileged access abuse. By taking a machine learning-based approach to validate every access request, the context of the request, and the risk of the access environment, roadblocks in the way of greater privileged user productivity disappear. Privileged credential abuse is greatly minimized.