Database examples

Real-life examples of hyperautomation show the business value of AI

Companies have a wide range of new technologies to help automate as many of their processes as possible while allowing human workers to do human work: a concept called hyperautomation.

Hyperautomation is a catch-all term describing strategy-driven automation aimed at achieving both broad and deep process automation. It is undertaken specifically to help a company achieve overall strategic objectives, rather than being ad hoc and at the discretion of isolated individuals or teams.

Almost any type of automation tool can be – and should be – leveraged for such an effort, including the following AI-based tools: machine learning, natural language processing, image and pattern recognition, and others that traditionally require human attention, decision-making and action.

AI-powered hyperautomation pays attention and makes decisions

For many IT professionals, AI-powered tools continuously automate sifting through endless streams of security logs and alert data for threats and breaches. Therefore, cybersecurity particularly benefits from the application of machine learning and other techniques. Such tools drastically reduce the number of false positive alerts sent to security teams.

These AI-powered tools can also trigger other parts of a largely automated process, including escalating tickets in an internal tracking system, alerting service providers when indicated, and notifying of a response team appropriate to the situation outside of the Security Operations Center (SOC) if required.

The scope of hyperautomation examples extends beyond IT-related systems and operations. For example, it may encompass physical security in addition to cybersecurity. By using image recognition to analyze outputs from security cameras, apps can flag in-frame activity that requires human attention and suppress alerts resulting from non-threatening activity. Specifically, they can transmit an alert for a person approaching a security fence, but suppress an alert for a deer or dog.

Once the decisions are made, hyperautomation takes action

If organizations continue to build enough trust in their AI assistants, they can empower these detection and assessment tools to decide that an event requires a response and trigger activity beyond alerts, notifications, or tickets. They can trigger playbooks in SOAR systems, for example, which may themselves be equipped with AI and capable of crafting or activating the necessary policy to be implemented on the fly.

AI-based tools can even directly assess and implement appropriate countermeasures rather than executing predefined manuals prepared by staff, for example:

  • identify the types of access to block and create blocking policies for all available enforcement points;
  • identify systems to isolate and send commands to switches or network controllers to switch them to different VLANs or security groups; and
  • identify user accounts to quarantine and apply new policies to temporarily block their access to sensitive systems.

Hyperautomation capable of understanding humans

Although it often receives less attention than things like machine learning or deep learning, natural language processing (NLP) makes important contributions to examples of hyperautomation so far. NLP is the central discipline behind “chatbots” of all kinds, and bots find their way into computing and non-computing processes.

IT teams are most familiar with NLP chatbots acting as the first point of contact for the service desk, most often in a text messaging channel, but sometimes in the form of interactive voice response systems. Additionally, AIOps vendors incorporate some of the same features into their tools. This allows personnel to use natural language queries to run analyzes and reports on network and system monitoring data to get answers verbally rather than simply in charts and tables. This type of “virtual assistant” is becoming increasingly common and not only provides answers to questions, but also suggested courses of action to address issues.

The bottom line is that hyperautomation involves all types of automation and processes, but to truly develop full automation of any process currently involving human decision-making and attention to communications – spoken or written – the tools of AI are essential. We can expect future hyperautomation examples and use cases to attribute their success to AI systems.