Analyzing Identity and Access Management (IAM) through the lens of data management

An organization's identity data today is its linchpin. This invaluable asset binds an enterprise together, orchestrating access rights, establishing a unified and accurate view of users across various channels, and empowering informed security decisions.

However, the identity infrastructure is continuously becoming more complex as businesses expand their digital landscape, bringing more users, devices, and applications within their IT environment. With this increasing complexity, poor data management can engender substantial financial losses and jeopardize sensitive user and customer data.

The well-established principles of data management, encompassing data cleansing, validation, linking, enrichment, and deduplication, offer a viable roadmap for effectively managing identity data. However, when you consider the variety of attributes and reliances identity data has, it is no surprise that these principles can differ in scenarios of identity data versus other data sets.

So, let’s discuss how these traditional pillars can be adapted and optimized to fortify identity data management, thereby enhancing security, achieving cost efficiency, and fostering informed decision-making.

Applying the Five Pillars of Data Management to Identity Data

The majority of data, in general, is a crucial asset for any business, as its effective use can improve the customer journey, the buying processes, and broader business decisions. However, the stakes are even higher when it comes to identity data -- this could be a person, an application, or even a service account. If these identities are not accurately verified, your system could be at risk for a wide range of malicious threats.

To effectively apply and optimise the five pillars of data management for identity data, you must first ensure data quality, or in other words, clean the data. If any information is missing, it can halt critical decisions such as granting access permissions, which require manager approval. But it's not just about having data, it's about trusting that data. You need to be able to verify its accuracy and reliability.

Linking together different sources of information also highlights the importance of identifying the 'truth'. You need to confirm which data sources are reliable, whilst avoiding unnecessary data duplication. This used to be a manual job, but more and more, we are able to leverage automated solutions. These resources can spot issues, highlight risks, and focus on changes in data over time.

Enriching data is key. For general data, enrichment means merging trusted third-party data with your existing database to make the information more comprehensible. Simply put, it helps achieve a complete picture of your existing data. However, in the context of identity data, enrichment is more about adding extra attributes to existing identity. It helps to clarify the extended privileges and restrictions of a certain identity, so they can’t be misused or abused in any manner.

In a Zero Trust approach for example, the richer the data, the better. Ideally identity data includes role-based details, as well as more nuanced data like specific qualifications or even travel plans. The more varied and detailed the data, the more context we have for decision-making, which reduces false alarms and allows for more precise permissions.

Finally, effective IAM requires meticulous de-duplication. Duplicates across multiple systems can stop everything in its tracks. It's crucial to have a system that can find these duplicates and effectively manage them. So, it is important that your IAM infrastructure is able to apply these nuances of data management to identity data to ensure robust efficiency and security.

Visibility over Identity Data -- A Tool for Cybersecurity and Cost-Efficiency

Understanding the adapted pillars of data management for identity data is just the first step to effective IAM. The next involves using these practices to gain visibility over both human and non-human identities. It is about answering two fundamental questions: who has access to what, and do they actually need that level of access.

Visibility in this context serves a dual purpose. On the cybersecurity front, understanding who (or what) has access to certain resources helps identify potential weak points in the security landscape. For instance, a human identity with more access privileges than necessary can become a prime target for threat actors. Similarly, a non-human identity that is poorly managed could potentially be exploited for a cyberattack. By having a clear picture of these identities and their access rights, organizations can anticipate potential security threats and take preventive measures.

On the financial front, managing identity data efficiently can lead to significant cost savings. Consider an example where a user has a top-tier license for a platform like Azure, granting them access to a suite of services. If their role only requires a subset of these services, the organization could be wasting funds on unnecessary access rights. By gaining visibility into what services are actually being utilized by an identity, organizations can optimize their licensing costs.

Furthermore, companies can take advantage of data analytics to compare granted access versus actual usage. By tracking what tools a particular identity actually uses and cross-referencing this with their granted access, organizations can streamline access rights, enhance security, and potentially cut costs.

Streamlining and Automation -- The Path to Continuous Control

Achieving effective control over identity data isn't a one-and-done affair. It's a journey that requires continuous effort, adopting a phased and iterative approach that avoids the daunting task of 'boiling the ocean'. The process begins by focusing on certain sets of data within specific platforms, gradually expanding the scope as your data management processes mature.

The first phase involves gaining access to the data and understanding its structure and relevance. This is followed by correlating data from different sources to identify patterns and commonalities and then cleaning the data to ensure accuracy. Filling in the gaps, managing the lifecycle of data, and continuously improving the process completes the cycle. Each subsequent phase incorporates additional platforms and facets of your organization’s identity data, evolving the processes and controls as necessary.

Automation plays a crucial role in this endeavor. With the aid of machine learning and AI, much of the data gathering, cleaning, correlation, and analysis can be automated. This reduces the burden of manual effort and minimizes the potential for human error. As these tools become more sophisticated, they offer an 'easy button' that can cover an increasing portion of the process, helping to streamline operations and improve efficiency.

By methodically applying these processes and harnessing the power of automation, organizations can maintain a consistent cadence of control over identity data. This, in turn, allows them to quickly respond to emerging threats, optimise their resources and reinforce their overall security posture. The ultimate goal is a state of continuous, proactive security, facilitated by the effective management of identity data.

Image Credit: Wayne Williams

Wade Ellery is Field Chief Technology Officer with Radiant Logic. Wade has over 20 years of increasing responsibility and experience in Enterprise IT direct and channel software and services sales and management. He holds in-depth knowledge and experience in enterprise IAM, IAG, Risk and Compliance, and IT Security products.