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Real-world hyperautomation examples show AI's business value

Hyperautomation examples in the real world help businesses automate as many of their processes as possible and achieve their strategic goals. AI is instrumental in these efforts.

Businesses have a breadth of new technologies available to them 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 catchall term describing strategy-driven automation aimed at achieving both broad and deep automation of processes. It is undertaken specifically to help a business achieve overall strategic goals, rather than being ad hoc and at the discretion of individuals or teams in isolation.

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

AI-based hyperautomation pays attention and makes decisions

For many IT professionals, AI-based tools steadily automate sifting through endless streams of security log and alert data in search of threats and compromises. Therefore, cybersecurity is especially benefiting from the application of machine learning and other techniques. Such tools radically reduce the number of false-positive alerts passed through to security teams.

These AI-based tools can also trigger other parts of a broadly automated process, including elevating tickets in an internal tracking system, alerting service providers when indicated and notifying a situation-appropriate response team outside the security operations center (SOC) when appropriate.

The scope of hyperautomation examples extends beyond IT-related systems and operations. For example, it can encompass physical security in addition to cybersecurity. Using image recognition to parse the outputs of security cameras, applications can flag activity in-frame that requires human attention and suppress alerts resulting from nonthreatening activity. Specifically, they might pass through an alert on a person approaching a security fence, but suppress an alert for a deer or dog.

Once decisions are made, hyperautomation takes action

Should organizations continue to develop sufficient trust in their AI assistants, they can empower those detection and assessment tools to decide that an event requires a response and trigger activity beyond alerts, notifications or ticketing. They can trigger playbooks in SOAR systems, for example, which may themselves be AI-equipped and able to craft or activate the necessary policy to be put into effect on the fly.

AI-driven tools can even assess and implement suitable countermeasures directly rather than running predefined playbooks prepared by staff, for example:

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

Hyperautomation capable of understanding humans

Although it often gets less attention than things like machine learning or deep learning, natural language processing (NLP) is making important contributions to hyperautomation examples thus far. NLP is the core discipline behind "chatbots" of every description, and bots are finding their way into both IT and non-IT processes.

IT teams are most familiar with NLP chatbots taking the role of 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. Also, AIOps vendors are integrating some of the same functionality into their tools. This allows staff to use natural language queries to run analyses and reports on network and systems monitoring data to get answers back verbally instead of just in charts and tables. This sort of "virtual assistant" is growing steadily more common and powering not just answers to questions but also suggested courses of action to respond to problems.

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

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