Within the average company, non-human identity (NHIS) has now outperformed employees, contractors and customers in anything between 10-1 and 92-1.
In addition to this, fragmentation of human identity management due to allowing one person access to multiple on-premises, cloud computing, and hybrid environments, and enterprise identity and access management (IAM) becomes extremely difficult.
I don’t trust anyone
The concept of applying a zero trust policy to reduce the risk of unauthorized access to the corporate IT environment has been promoted for almost 20 years. This includes defeating hatches by removing everyone’s access to corporate assets and applications every day and reinstate those qualifications at the start of new work days.
Zero Trust Policy is not just paranoia: Cyberattacks have been launched through non-human identities as diverse as retail HVAC units (target violations) or aquarium thermostats (an American casino violations).
Block bad behavior
You can configure an IAM platform to consume information from Enterprise SIEM and make decisions about whether access should be restored to a particular entity as part of identity, governance, and management. This IAM/SIEM integration allows organizations to quickly block privileged access based on unusual behavior. Similarly, IAM can feed data into a SIEM environment and contribute to the audit trail.
Everyone is equal, but more equal than others
The problem with zero trust in the all-human workforce is that hierarchy and political civil wars inevitably lead to delays in fully enforcing policies.
IT administrators often find it difficult to deny access to senior executives. This could open loopholes within the system and be exploited thoroughly through spear phishing and whaling attacks against privileged identities.
Learn from experience
Fifteen years ago, many privileged accounts had static passwords and activity was not actively monitored by these accounts, creating a great risk if these accounts were compromised. A widely reported example was the SCADA system at a hydrotherapy plant in Florida. This was accessed remotely by an unknown person using a shared password that attempted to increase the concentration of lye added to the town’s drinking water. Thankfully, the unauthorized activity was quickly discovered by an eagle-eyed employee who had closed access.
Incidents like these have facilitated the broad implementation of privileged access management tools. This provides an audit trail for the activity and revokes privileged access after use.
Computer says no
Management of non-human identity can seem overwhelming. However, if an organization applies agent AI and machine learning, its nonhuman identity infrastructure could simply follow zero trust rules and create a much faster response system. Agent AI, which handles the access deletion and repair process, is not bullied to bending rules, even the most privileged individuals.
By applying agent AI and machine learning, zero trust can be achieved, allowing businesses to achieve greater ROI with passwordless technology.
The warning to AI-Managed Zero Trust Access is that rules must be clearly defined and limits must be set correctly. However, there are significant potential benefits to applying machine learning and agent AI to respond quickly to security situations.
Realize ROI from RPA
In a typical company, system access for at least 1,000 employees is managed by the organization’s Joiners, Movers and Reaver models. However, in more complex environments, this can create a greater administrative burden. To address this, organizations are turning their eyes to robotic process automation (RPA) and performing recurring tasks such as resetting passwords.
As an example, the large financial institutions we worked with applied an RPA solution that quickly executes automated password control and provides an audit trail through the product via API. With RPA, organizations have been able to implement automated, complex password creation more quickly, reset it into their environment, and enjoy increased productivity.
Realize ROI from AI
The following frontier applies AI to corporate governance policies on authorization and authentication that create more streamlined models in a way similar to AI applications in the networking space to measure corporate security attitudes.
Continuous service improvement within ITIL, as well as periodic improvement processes within Agile and DevOps will also benefit from AI. Analyzing the entire enterprise ecosystem to understand who uses what, when, and where, and using machine learning and AI to change and adapt policy and governance can lead to automated, circular improvement processes.
With the supervision of existing Enterprise Committees, there is no need to continually redesign the system, and the ROI needs to be increased with improved efficiency and security through the full adoption of zero trust and passwordless technology.
NHIS management and identity will be sprawled with AI
AI-enhanced identity, governance, and management (IGA) technologies allow you to continuously analyze access between each individual and NHI, and dynamically adapt access according to the behavior of that person or entity. As an example, if someone leaves the building for the day, they can revoke their access until they come home or log in from their home IP address. If AI is not compromised, it is much more difficult to infiltrate someone’s work environment using this type of AI-enhanced IGA.
The system has already flagged it within a time frame that is physically impossible to achieve, such as the identity of trying to log in to the same system from two different aspects of the Earth. However, without a certain level of surveillance, things will slip through the cracks.
In a typical corporate environment, it is far too overwhelming to consider all these vectors and ask each person to request access to everything they need to complete their daily tasks. This painful user experience will lead to workarounds and bad practices, such as simply leaving a sensitive application open after use.
However, when AI is used to handle all the mediocre checks, administrators can focus their energy on setting up well-defined rules. An example would be to set rules to prevent access earlier than 6am. If an employee needs access earlier than that, they can program AI to ask a few questions that only that employee can answer. This can be backed up with a Thumbprint or Physical Token request to reauthorize access to a particular application.
The system then goes through and everything goes back. This is where private automation in the face of AI and machine learning offers enormous productivity and security benefits. You can apply AI to run very basic functions and plain languages for converting documents.
Of course, cyberattackers are using AI to develop more sophisticated methods to avoid defenses. Counting AI with AI is one of the advanced features of what analyst company Kuppinger Cole calls identity fabric. Within the identity fabric, AI and machine learning algorithms enhance the implementation of risk-based authentication by dynamically adjusting security measures to new threats.
Future Framework: NHIS, Humans, and AI
Identity Fabric coordinates and automates various parts of efficient and robust identity management. This includes providing self-service options for users to reduce help desk requests for password reset and access issues.
A combination of automation, self-service and best practice enforcement increases operational efficiency. Teams no longer have to manually manage fragmented identities, reducing overhead. Operational efficiency changes to cost-effectiveness when integrated identity fabrics remove the need for payment and management of redundant identity management tools and fragmented solutions.
Centralized identity control reduces identity silos and closes security gaps. AI-driven surveillance helps detect identity attacks early before they become incidents. Insider threats are mitigated by implementing zero trust and augmented by adaptive authentication to protect sensitive data.
Based on the evolution of IAM to date, we predict that as the number of NHIS continues to increase, AI models are integrated to provide additional visibility and surveillance for identity properties. Modeling security infrastructures that govern non-human identities, first performed by AI and then refined by human administrators, have the potential.
