Identity Analytics

The need for Identity Analytics

Globally, cyberattacks are still on the increase. Really, that is not news. Yet, the epidemic that started in early 2020 demonstrated that cybercriminals don’t think twice about using catastrophic occurrences to their advantage even as they endanger mankind. It worries me.

The lack of awareness of threats and the quick development of cybercrime tactics are the two aspects that enterprises should find the most unsettling from a security perspective.

On the one hand, organisations of today must manage a sizable number of access privileges for each user across several apps. On the other hand, data breaches brought on by credentials that have been stolen are at an all-time high. It is obvious that the identities of users and their privileges pose a significant danger to a company.

Organizations require identity analytics to quickly explore and assess the risk posed by user identities and associated entitlements.

What is Identity Analytics?

Big data, artificial intelligence (AI), and machine learning (ML) technologies are used by identity analytics solutions to crunch data from diverse sources and provide actionable insight where identity-related data exists. An organisation can learn what resources their users have access to, see how they are using their access rights, track unusual user behaviour, and determine if they should be given access based on contextual information such as geolocation, device type, and so on, using the advanced analytics and dynamic risk scores provided by identity analytics tools. All of this is done in real time.

A solution for identity analytics may also automate tasks like verifying access and fixing policy breaches. Manually carrying out these procedures would take too much time, be labor-intensive, and increase operating expenses. Automating these processes aids businesses in streamlining compliance audits.

Identity analytics tools use cases

Find and eliminate any extraneous access rights

Users should ideally only have access to servers, directories, apps, or services that are relevant to their respective job tasks. Unfortunately, for a variety of reasons, many users tend to have a lot of permissions.

It could be as a result of a promotion, a change in positions, or being given special authority to complete a particular duty. All access rights are examined using identity analytics based on user behaviour and application usage trends. Profiles with excessive access rights are immediately identified, and any unused access privileges are promptly removed.

Access certificates based on risk

Many users in today’s firms have excessive access privileges. Yet, individually assessing each of these rights takes time and might result in hasty, rubber-stamped approvals that ignore possible security risks.

Identity analytics technologies calculate contextual risk scores for each user based on a variety of factors, including user behaviour, application usage statistics, and peer group analysis. Some technologies even provide risk assessments at the entitlement level. Identity analytics systems may be customised to notify managers exclusively of high-risk user profiles. This significantly minimises the amount of time managers must devote to certification efforts. Managers can do more effective certifications since most identity analytics products give a context-rich unified view of a user’s entitlement data collected from many systems and apps.

Improved oversight and security of privileged accounts

In most companies, there are two main categories of privileged accounts: service accounts, which are used by programmes or system processes to communicate with the operating system, and user accounts with administrative capabilities. Due to the ease with which sensitive data from a business may be accessed through these accounts, cybercriminals target them.

With the use of identity analytics technologies, you can quickly identify underutilised privileged rights and detect alterations to privileged accounts, including efforts at privilege escalation and credential sharing.

User Behavior Analytics (UBA) is used by identity analytics solutions to identify these atypical user behaviours. UBA uses machine learning techniques to construct a baseline of usual behaviours particular to each privileged account, then detect deviations from the baseline, and lastly inform the appropriate individuals. Imagine when a user account in Active Directory (AD) is supplied with only one administrative permission. If this account unexpectedly gains additional rights, such as resetting passwords, changing owners, removing child objects, and so on, UBA will notice these unusual activity and flag the account as suspicious. When unexpected activity is noticed, identity analytics technologies allow IT managers to establish automatic actions, such as temporarily limiting access.

Detection of breaches of the division of duties

Separation of duty (SOD) is an internal security procedure that makes sure no one person has total authority over all of a resource’s or process’s functions. Since future fixes and security upgrades won’t function if a developer changes the source code and the software becomes unstable, for example, developers shouldn’t have admin credentials to production databases. When user accounts have conflicting access rights, SOD violations happen.

They represent a security risk because they indicate that there are employees in the company who have the ability to alter data covertly.

When a SOD violation is found, identity analytics tools can instantly stop account access and inform the IT security staff. Identity analytics technologies guarantee that SOD violations are quickly recognised and the cause of each violation is discovered. Managers will have a better understanding of their users’ account entitlements and access rights. When it comes to access requests, more visibility allows managers to make better judgements.

Risk reduction with adaptive authentication

MFA requires a user to validate their digital identity by authenticating themselves using at least two factors other than their user credential.

A company-wide MFA typically creates an authentication barrier for users who are already within a secure network, which can degrade user experience and productivity even when the additional steps of verification increase security.

Real-time user risk evaluations are conducted by adaptive authentication systems, which only ask users to give an extra authentication factor when they believe their risk to be high. Organizations may increase security with adaptive authentication without sacrificing usability.

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