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Securing SaaS? Learn About Context-Based, Self-Supervised Learning

An estimated 70% of business apps used by organizations are SaaS-based, and that number is rising. For many businesses, this has unquestionably increased productivity, efficiency and teamwork. Nevertheless, it has also increased the attack surface and opened up new entry points. Information about the users and data for the SaaS applications many enterprises are using is not generally visible. It’s frightening because it’s difficult to secure and defend something you can’t see and may not even know about.

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IT departments require a method for enforcing security regulations and making sure that these tools aren’t being used improperly to transmit sensitive data. And, they must be able to accomplish this with minimal disruption to productivity and efficiency.

It may be appealing to attempt to address SaaS security issues by merely implementing “automation” and establishing a few general principles, but the drawback of this strategy is that occasionally, you throw out the good along with the bad. That is, you risk blocking employees from carrying out necessary tasks, including disclosing critical information on an as-needed basis. Here, the concept of self-supervised learning can assist in contextually applying rules and policies.

Data access is never one-size-fits-all

Automation is essential for tackling the problem of safeguarding SaaS data, but it is impossible without the right context.

Imagine setting up your system such that it will immediately stop or prohibit any exchange of sensitive data. Here is when the strategy falls short: When someone communicates sensitive information, it’s usually because they have to do so in order to do their job, particularly if they work in a department like finance or human resources that handles a lot of sensitive data. The efficiency of these departments’ operations may be significantly impacted by a workflow that automatically forbids the sharing of sensitive information.

Even should you adopt a more cautious approach, such as setting things up such that users will lose access to sensitive information after a certain amount of time, it can still have a detrimental effect on workflow and productivity. Also, the issue of first securing access to that sensitive information remains unresolved. When it comes to SaaS tools and data, automation cannot simply be deployed extensively and uniformly.

The context is what you need.

Making the process contextual

Automation in context can help reduce risk and address problems without creating additional friction. It’s a means of striking a balance between security and economic objectives.

While assessing an activity to see if it is appropriate, context is the ability to understand the greater environment. If a security team has this information, they can figure out who works with whom and who is authorized to use specific data, tools or systems and can assess if a particular activity is appropriate.

With the data it has been given, the model can train itself using a self-supervised learning strategy. It doesn’t need highly specific labels or instructions from people. One application is to use self-supervised learning to examine the relationships between staff members and comprehend the communication and collaboration patterns inside an organization. To enhance security and safeguard sensitive data, the model can learn about standard activity and spot any odd or anomalous behavior. It can also assist in offering a more precise and efficient method of mapping sensitive data.

Beginning the self-supervised learning journey  

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Incorporating self-supervised learning in SaaS security shares similarities with the training approach of large language models like ChatGPT. While language models learn from a vast body of data from the entire internet to predict the next word in a sentence, self-supervised learning in SaaS security can be tailored to train on the entire organizational social network graph. This process enables the model to anticipate the next interaction in the graph based on prior interactions.

By capturing the unique communication patterns and collaboration dynamics within an organization, the self-supervised learning model becomes more proficient at pinpointing potential security risks and ensuring accurate data protection measures are being taken – without disrupting essential business processes.

Your primary collaboration platforms must first be connected via an API such as Google Workspace, O365, Github or Slack. The history data will be processed using self-supervised learning’s advanced analytics and used to train the system. Once the algorithm has been taught, you can start utilizing it to track business operations and spot security threats. The algorithm will keep learning and adapting.

You can create regulations and procedures that are particular to your company once the self-supervised model has a solid grasp of the organizational context. Additionally, you can use that data to give your current policies and automated workflows greater context.

The analytics will automatically identify the exposure of sensitive data based on the context it has learned, which is another advantage. Security analysts will observe the output of the self-supervised learning system and adjust the data or business rules and actions that it creates, as necessary.

Automated learning plus human experts

It’s critical to remember that self-supervised learning does not take the place of human supervision and analysis. Security analysts should go over the model’s output on a regular basis and apply their own knowledge to come to a final judgment regarding automation and security policies.

Working closely with specialists in security and the related business areas is crucial to achieving the best outcomes and ensuring that the model is correctly configured and put to use. This will enable you to both protect the sensitive data belonging to your company and make the most of your security software.

Your self-supervised learning analytics can be a potent weapon for safeguarding SaaS apps if you use the appropriate strategy. Because no two organizations are the same, understanding your organization’s particular demands is the first step in configuring automation and rules based on a context produced by self-supervised learning. Understanding the normal user behavior on an organizational social network graph and recognizing the sensitive data that must be protected are two examples of this.

Furthermore, it’s vital to guarantee that the model is comprehensible and reliable and to be open and honest about how it makes decisions.

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SaaS security, self-supervised 

We certainly don’t want to stop the various beneficial ways that SaaS tools and apps have altered the workplace. But businesses must carefully consider the security implications of these apps and the private information transferred between and among them. By offering a more flexible and context-aware approach to security, a self-supervised learning strategy can bolster your organization’s security stance. The system’s capacity to continuously learn from and adapt to your organization’s changing environment makes it easier to recognize and manage security threats while enabling regular day-to-day business operations.

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