An internal threat refers to the risk of somebody from the inside of a company who could exploit a system in a way to cause damage or steal data.
The impact of Artificial Intelligence on IAM
Artificial Intelligence and Machine Language are making a big impact on the security industry.
It is clear that artificial intelligence (AI) is quickly becoming increasingly important in cybersecurity and identity and access management. However, many organizations wonder how AI can serve as a lever to improve their IAM workflow.
The importance of a mature IAM approach
As today’s business environment is global and highly interconnected, the probability increases that an organization becomes the victim of a data breach or another cyber threat. Deciding who should have access to what information is difficult for many businesses and this leaves their systems vulnerable. Fact is that the importance of a clever and mature Identity & Access Management (IAM) strategy shouldn’t be underestimated.
According to a Forrester research report, 83% (!) of organizations do not have a mature approach to IAM. The risk that these organizations get confronted with a data breach is twice as high compared to organizations that have their IAM strategy in order. The report also shows a direct correlation between smarter IAM approaches and reduced security risk, improved productivity, increased privileged activity management and greatly reduced financial loss over their less mature counterparts.
A typical IAM issue is that users are given access privileges based on their role in an organization, but employees rarely fit into single roles. They may need special one-time access or each person fulfilling the same role might need slightly different types of access. This results in very complicated situations often requiring collaboration between many departments. Proper management thus involves many employees across all layers of the organization. This could lead to a situation where people might suffer from a so-called “security fatigue”, because of a high amount of technical data, a difficult decision-making process and a lack of relevance with their day-to-day job. Disastrous consequences for businesses as a result of a poorly managed IAM infrastructure lure around the corner.
How can AI improve this situation?
Although this situation is quite common in many enterprises, it doesn’t have to be so. AI and machine learning (ML) technologies can be a major help for effective IAM and can help to avoid a lot of frustration. These technologies can assist enterprises to grow from an overly technical approach of access management into a form of access management that is understandable on all levels within a business.
Analytics combined with artificial intelligence can provide more focus and contextual insights so that both technical and non-technical employees can work more time efficient. Modern technologies provide ways to learn new insights and automate processes, which will drastically speed up the existing IAM compliance controls. They can detect anomalies and potential threats, without the need for a large team of security experts. This gives employees, both technical and non-technical, the information needed to make correct decisions. Especially in the area of anti-money laundering and fraud detection, but also in the area of combating insider threats, such progress is crucial. It also paves the way to move from reactive access management to preventative or even corrective access management. This results in enterprises being continuously in control, continuously secure and continuously compliant.
Although artificial intelligence and machine learning provide many benefits, people often mistakenly assume that this technology can take over all the work and automate the whole process of IAM. As of today, this is not the case. In fact, these modern technologies prove to be most useful when they are implemented to do one task instead of many. So, while full automation is not yet possible, AI and machine learning can definitely help and improve identity and access management.
Elimity’s approach to AI in IAM
Elimity uses machine learning to help with insights about the current identity and access state of your business. Machine learning algorithms are strong in detecting anomalies and assisting with setting a so-called baseline model. This model is translated into rules within Elimity. Next, these rules can be verified by the appropriate people in the context of specific audits or reporting efforts. If needed, the rules can be updated to better align with the real situation within the organization and to take the business context into account. All these rules and the anomalies that are discovered within the current state will be used in the evaluation of all future reporting. We don’t believe in a big bang approach of AI. A lot of business context and knowledge is not covered in the tooling and configuration and is thus impossible to be automatically discovered. We are a strong believer of the additive role of AI: we apply machine learning as a virtual assistant next to an expert, to help to dig through the data and to discover what is baseline and flag anything unusual for human review. This virtual assistant will help to automate the IAM controls to be more continuous in control.
PS: Please don’t forget to take a look at Elimity Insights. This powerful, yet easy to use SaaS tool uses machine learning technology to get in control fast and to stay on top of the ever-changing regulatory requirements.