Cross Identity: Converged IAM Solutions for Enhanced Security

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Identity Analytics for a Safer Workplace

identity analytics

The British Statesmen and Philosopher Edmund Burke once said, “Better be despised for too anxious apprehensions than ruined by too confident security.”

This quote quite aptly fits the approach that should be taken for security today, even though the quote dates back to the 18th century.

Today, organizations are inclined towards security protocols like zero-trust policy, and rightly so. In this security model, every interaction with your network, be it internal or external, is considered a threat unless verified as secure. This means the security perimeter is not just a firewall any more that separates your organization from the outer world.

Instead, they are smaller perimeters set internally, with the least privileges among employees, which ensures productivity along with security.

This also means, there is a dire need for security measures to continually monitor organizational activities on every minuscule level. Whether they are accompanied by anxious apprehensions or not, they must always be with insights that inform security decisions and not blatant confidence in security.

How much can your IT handle?

Each user is entitled to several applications when they join, the prerequisites set by the managers in agreement with HR and IT. Through an employee’s life cycle in the organization, they require several additional accesses, which may or may not be revoked after usage at the right time.

This is a basic layer of user’s interaction with applications. Now, what about their habits of interactions? Their regular time of login and logout, location, IP address, and amount of usage per day?

How aware are they of these habits? How capable are they of identifying anomalies in this behavior? Assuming the user is tracked stringently, what about their password habits?

How well versed are they with the users’ interaction with the web? You might have blocked a couple of websites that are deemed unsafe, but how sure are you their passwords aren’t already in a database on the dark web?

Multiply this with thousands of employees having access to various resources in your organization. Will your IT team be able to track of all them? Even if they attempt this laborious task, they are bound to make errors along the way which could cost your company money, reputation, and data.

Fraud and human error cost UK businesses alone an estimated £98.6 billion a year, according to research.

The smaller businesses with fewer employees aren’t an exception to this either. 42% of small business owners stated that employee negligence was the leading cause of security breaches. Thus, it is quite evident that security should be an organizational priority, always, irrespective of their number of employees.

The Identity Analytics solution

Irrespective of how capable your IT team might be, singling out anomalies in a sea of data is an extremely cumbersome task and yields to a lot of time lost time.

Identity Analytics can solve this for you.

Identity Analytics is the enhanced, intelligent, Identity and Access Management (IAM) solution. The user data is collected from several sources and analyzed using the technologies of artificial intelligence, machine learning, data analytics using cognitive systems, and much more. These systems comb through the vast array of data to provide insights, using which you can make data-driven security decisions.

These technologies obtain data from the various sources that security solutions use. Access governance contains complete information about the entitlements and accesses within your organizations. Content-aware data loss prevention, which reads through the data in emails, or any information that leaves your network or comes to your network, encrypts sensitive data. This ensures there is no possibility of a security breach in these communications.

These sources, along with web, IoT, and database logs, can provide a wide range of data. Using this information, you can detect ongoing real-time anomalies and predict threats.

Monitor and predict threats

The landscape of security with analytics has an array of technologies that can provide security insights. Rules or baselines of user behavior are set against which the present data is compared—alerting whenever there is a derail from the ‘normal’ pattern. This data is monitored continuously.

Security Identity Event Management (SIEM) tool monitors user activities, activities on firewalls, servers, anti-virus software, provides reports, and alerts the security analysts of a possible security risk. The SIEM compares events against the set rules and notifies when there is a discrepancy in the data.

SIEM, when collaborated with IAM, can provide much more streamlined insights.

When an employee logs in at an unusual time and enters the wrong password multiple times, this might provide an alert from the SIEM. The IAM solution can now provide you with information on what the risk might’ve been.

Intelligent IAM systems have risk scores associated with every access, application, or assets related to the organization. Combining risk scores with the knowledge of whether the user was authenticated with a second layer of authentication can help analysts understand the level of risk because only a legitimate user would have the information to authenticate themselves using MFA. These insights can probably let you know if it is just a matter of a wrong password entered multiple times or something to wake the managers in the middle of the night.

User Behavior Analytics (UBA) goes a level deeper. UBA uses artificial intelligence, machine learning, and big data to understand the behavior of each user. UBA not only detects any diversion from the usual behavior but also alerts possible threat before it even occurs.

The world of AI, machine learning, and data analytics have indeed changed the face of security, and most importantly, identity and access management by bringing in ‘Identity Analytics.’

While risk scores provide an overview of which incident is capable of causing a high-risk threat, technologies like AI can automatically block access when authentication fails. Machine learning enhances the Adaptive or risk-based authentication processes by understanding the user behavior anomalies against the provided risk score. This insight leads to the raising of authentication levels in such instances. This ensures that every possible threat is met with the right amount of security before the analyst can even revoke the access.

In instances of SOD violations, these intelligent solutions are especially insightful. For example, a firewall configuration is changed at an unusual time, which typically requires multiple approvals. This incident is a cause for alert. But, if the authority to change a configuration and the person approving the change is the same, then this SOD violation can be looked into with a high alert and access can be blocked.

The future of Identity Analytics

Identity Analytics has indeed brought in a security revolution, combining advanced predictive technology with Identity and Access Management.

Cognitive systems are going one step further. It is the usage of Artificial Intelligence to its most potent form, to detect not only anomalies but also complex business decisions.

KuppingerCole, a leading analyst, calls it, “proper semantic interpretation and logical reasoning based on evidence.” They say it not only saves days but months of analyst work—making risk detection happen at a much faster rate.

With the increasing risk of security, you need intelligent security solutions. Solutions of Identity Management folds these advanced computing systems into its fundamental processes to provide ‘Intelligent IAM’ with Identity Analytics.

Brace your organizations with not only secure but smart solutions today.