I would suggest statistical methods (including machine learning): First, outlier detection. Then, approaches like “Association rules” (=not statistics to explain all the variance in a dataset but to find out tiny observations): for instance, they are useful for DNA prediction of diseases (one or two SNPs among millions of them), a forensic task.
When fraudsters know a tool (a template, a program), the solution is no longer valid. Research is the answer (research software rather than “production” software like in accountability). I mean, research as a step beyond production (only useful in the short term).
Search for a product comparison in Data Loss Prevention (DLP)
PAM Architect at a tech services company with 11-50 employees
MSP
Top 10
2020-12-23T13:11:29Z
Dec 23, 2020
This is an inside-out --- outside-in --- inside-in question, as an insider can be an outsider as well. There is no short answer other than a blend of a PAM tool with Behavioral Analytics and Endpoint Management, to protect credentials, govern activities, and detect abnormal activities.
I have about 40 questions I would ask before spitting out a single solution. Without knowing more about your environment I would be slow to start throwing possible solutions, as this will take you days to sort out the differing capabilities and features. You can start by looking at the Gartner Quadrants for PAM tools like BeyondTrust, CyberArk, Centrify, Thycotic, MicroFocus and others. If you spear your specific requirements you may miss bigger threats in your circumference, so use a net, and remedy the surrounding threats in this process.
Director InfoSec and Audit at a manufacturing company with 1,001-5,000 employees
Real User
2020-12-26T18:22:20Z
Dec 26, 2020
You'd need to break out better what you consider to be the types of insider threats. There is fraud; very different in an application system than insider activity that may be simply malicious or results in data loss. You need to identify a baseline of normal activity for each user across files, network, user behavior and the endpoint; correlate abnormal behaviour and lean false positives; that is your software and/or the CyOps team supporting you must.
Doing that begins to give you some use cases that you can then test to determine if they are important to you and can be supported by your choice(s) of solutions. There may not be one, there may be layers needed, but depending on your choice you may be able to get more in one than with other options. Feel free to contact me off list (LinkedIn) if you'd like a matrix that could be used in a product comparison.
Account-Manager at Consist ITU Environmental Software GmbH
Real User
Top 20
2020-12-30T18:03:49Z
Dec 30, 2020
Hello All,
I hope you had a merry Christmas.
In this case it is as simple as it is. Just take Proofpoint ObserveIT - many companies in the public and financial sector have been using it for years. By the way, it has GDPR conformity, that's especially interesting if you want to go for the EU or California. It's easy to install, easy to administer, and comes with a huge number of use cases. So the need for customizing is reduced to minimum. It prevents, detects, alerts and tracks all inputs with a minimum of storage needed.
Few Steps Phase 1, define the architecture and monitor all high-privileged users with the default setup. Then work with Proofpoint or local support to define gaps and customize use cases (only a few days)
Phase 2 roll out to next group of users and so on.
I apologize for this non-technical answer, but sometimes it really is this simple. You don't need to invent the wheel a second time :)
Would like to wish everyone here a Happy New Year this way. Please stay healthy
User and Entity Behavior Analytics (UEBA) is a type of cybersecurity solution that uses machine learning to monitor and analyze the behavior of users and entities (such as devices, applications, servers, etc.) in a network. UEBA can detect anomalous or malicious activities in real time and alert security teams or take automated actions.
UEBA solutions work by analyzing activity from network users and other entities, such as hosts, applications, data repositories, and network traffic. They...
I would suggest statistical methods (including machine learning): First, outlier detection. Then, approaches like “Association rules” (=not statistics to explain all the variance in a dataset but to find out tiny observations): for instance, they are useful for DNA prediction of diseases (one or two SNPs among millions of them), a forensic task.
When fraudsters know a tool (a template, a program), the solution is no longer valid. Research is the answer (research software rather than “production” software like in accountability). I mean, research as a step beyond production (only useful in the short term).
This is an inside-out --- outside-in --- inside-in question, as an insider can be an outsider as well. There is no short answer other than a blend of a PAM tool with Behavioral Analytics and Endpoint Management, to protect credentials, govern activities, and detect abnormal activities.
I have about 40 questions I would ask before spitting out a single solution. Without knowing more about your environment I would be slow to start throwing possible solutions, as this will take you days to sort out the differing capabilities and features. You can start by looking at the Gartner Quadrants for PAM tools like BeyondTrust, CyberArk, Centrify, Thycotic, MicroFocus and others. If you spear your specific requirements you may miss bigger threats in your circumference, so use a net, and remedy the surrounding threats in this process.
You'd need to break out better what you consider to be the types of insider threats. There is fraud; very different in an application system than insider activity that may be simply malicious or results in data loss. You need to identify a baseline of normal activity for each user across files, network, user behavior and the endpoint; correlate abnormal behaviour and lean false positives; that is your software and/or the CyOps team supporting you must.
Doing that begins to give you some use cases that you can then test to determine if they are important to you and can be supported by your choice(s) of solutions. There may not be one, there may be layers needed, but depending on your choice you may be able to get more in one than with other options. Feel free to contact me off list (LinkedIn) if you'd like a matrix that could be used in a product comparison.
Hello All,
I hope you had a merry Christmas.
In this case it is as simple as it is.
Just take Proofpoint ObserveIT - many companies in the public and financial sector have been using it for years.
By the way, it has GDPR conformity, that's especially interesting if you want to go for the EU or California.
It's easy to install, easy to administer, and comes with a huge number of use cases. So the need for customizing is reduced to minimum. It prevents, detects, alerts and tracks all inputs with a minimum of storage needed.
Few Steps
Phase 1, define the architecture and monitor all high-privileged users with the default setup. Then work with Proofpoint or local support to define gaps and customize use cases (only a few days)
Phase 2 roll out to next group of users and so on.
I apologize for this non-technical answer, but sometimes it really is this simple.
You don't need to invent the wheel a second time :)
Would like to wish everyone here a Happy New Year this way.
Please stay healthy
Best Regards
Norman
In addition to responsesfrom Xavier Suriol and reviewer1324719, also consider ObserveIT from Proofpoint.