Human and Machine Behavioral Insights for the Zero trust Identity architecture

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Welcome to the new age of Behavioural-based Identity Management for Humans and Machines

Sharelock enables an innovative bottom-up approach to Identity & Access Governance based on understanding user’s and machine’s behaviour rather than just relying on central IAM/ITSM policies.

Sharelock disrupts the business of Identity-centric anomaly detection based on its own patented machine learning algorithms.

Sharelock provides its Behavioral insights in three distinct domains:

Insider Threat and Fraud prevention include data exfiltration attempts, compromised accounts, data breaches, shared and ghost credentials.
Compliance recommendations: based on actual user behaviours, aim to close the gap between the ‘should be’ state – described in Identity Management platforms – vs the ‘as-is ‘.
Machine Anomaly detection, detecting behavioural anomalies of any non-human related entity or attribute. This capability is targeted to Application Owners, providing behavioural anomaly detection monitoring for Compliance purposes.

Insider Threat and Fraud prevention

Sharelock Threat and Fraud prevention capabilities allow to spot anomalous & risky behaviors, understand the risk stage, and investigate the dangerous situations at the user level. Sharelock enables a set of powerful user-centric insights for investigation purposes, such as timeline analysis, peer-analysis, and behavioral analysis.

Compliance recommendations

Sharelock access compliance allows you to measure and reduce the ‘gap’ between the ‘should be’ vs. the ‘as-is’ on access provisioning policies. In simple terms, companies use Identity Management platforms to define and enforce what their users should have access to, according to their duties and roles. Unfortunately, users may either obtain ‘out of policies’ access permissions or be over-provisioned. Both situations create a ‘compliance’ issue, which often proves to be the ‘early signal’ which then turns into a subsequent insider threat security incident.

Sharelock provides insights and spots non compliant users, comparing any given user with its peers, then recommending remediation actions to achieve a compliant and secure ‘access posture’.

Machine Behavioural Anomaly detection

Sharelock Machine Anomaly detection enables behavioural monitoring of any non-human related entity or attribute captured in the ingested activity log files.

It can be applied to many use cases and ideally suits application-specific behavioural monitoring for Compliance purposes, providing Application Owners with essential Security & Compliance insights supporting their involvement in regular audit activities.

Typical use cases are

Monitor anomalies in the overall utilization or working hours habits of critical applications such as SAP or other compliance (e.g. Sox, GDPR).
Detect punctual anomalies on a named set of risky transactions across the entire user population.

Discover our wealth of benefits!

Zero-days start

Forget about rules, the system learns and sets behavioural baselines automatically. Forget also about preparing training datasets, Sharelock operates unsupervised. Sharelock saves at least 4-5 months of project efforts and costs compared to old-style products.

Massive reduction of false positives

Unlimited Identity-centric Indicators of Behaviour reduce the likelihood of false positives by over 21% compared to other ML-powered solutions.

Native Integration with IGA platforms

Infuse Sharelock Behavioral Risk Insights into your IGA platform , and enable Line of Business managers to be part of your extended security team, evaluating selected anomalous behaviors early on rather than late on. Inside the IGA UI they use every day.

An open and evolving ML catalog

Sharelock ML model catalog is an evolving creature and keeps the pace with state-of-the-art ML models. Sharelock adds new ML models to the catalog, without any impact on the underlying technology and client skills.

A pricing model you can control

Forget about EPS, Terabytes, and other opaque pricing models.
Sharelock pricing model is based on the number of Identities, plus connected applications.
Simple metrics you can control.

Patented Technology

Sharelock has patented its behavioral models, proving our unprecedented thought leadership. Few vendors in this space can claim patents.

The voice of the Customer

“We live and breathe user behavioral insights. We have seen plenty of Cybersecurity vendors talking endlessly about Machine Learning and AI, but none of them could fit with our agility requirements.

When we tested Sharelock, we finally saw the simplicity we were looking for and an actionable approach to Identity-centric Threat prevention”.

(CISO, Global Financial Information Provider)

Sharelock vs. Competition

Sharelock has designed its behavioral platform with a bright design principle: innovate over existing players to maximize accuracy detection and dramatically reduce false positives.

SHARELOCK

COMPETITION

THE SHARELOCK ADVANTAGE

ML Models

"Do we need to build training dataset for your Machine Learning Models?"

SHARELOCK

UNSUPERVISED

COMPETITION

SUPERVISED

THE SHARELOCK ADVANTAGE

Sharelock does not require any labelled training dataset (e.g. log file where anomalies are tagged”). Over 4-5 months efficiency in activation costs & efforts.

Behavioral baseline

"How is the behavioral baseline defined?"

SHARELOCK

USER BASELINE

COMPETITION

DATASET BASELINE

THE SHARELOCK ADVANTAGE

Sharelock does not require a minimum amount of users to run the ML models efficiently. Likelihood of False Positives reduced by over 21%.

Identity enrichment

“How do we enrich Identity-less log records?”

SHARELOCK

Machine Learning models and Identity Integration

COMPETITION

Just Identity Integration

THE SHARELOCK ADVANTAGE

Sharelock allows enriching log records with Identity attributes even in the absence of information such as account name or other user-related attributes. Prompt detection, reduction of the Investigation efforts.

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