Intelligent automation is a business-driven approach that organizations use to rapidly identify, vet, and automate as many business processes as possible. The platform encompasses the orchestrated use of multiple technologies including process mining, robotic process automation (RPA), intelligent document processing (IDP), artificial intelligence (AI), enterprise content management (ECM), also known as content services, and business process management (BPM). The low-code/no-code tools and packaged software solutions are fueling new ways to automate both back-office processes and customer-facing experiences at a rapid pace.

In the past several years, RPA has led the charge to these transformation initiatives providing the connectivity to both modern cloud-based and legacy systems where content is stored. Where manual work may have been performed in the past a human swiveling between applications, RPA bots filled a need to connect systems where integration either did not exist or lacked the functionality required.

The success of digital transformation projects is largely focused on the core technology and its ability to learn and adapt to the complexities of a business process and the unstructured nature of the data.

However, security and oversight of the intelligent automation technology stack often take a back seat to other aspects of the technology platform when it comes to automation. Incomplete audit trails, lack of oversight of automation when it comes to bot creation and activity, and AI models that change without proper understanding pose risks to businesses. The movement of data between systems handled by automated bots makes managing an RPA platform complex and difficult to deliver proper monitoring oversight and alerts to operations, IT, and the business.

Intelligent automation is moving so quickly that enterprises risk betting big on the core technology. There is an expectation that a patchwork of monitoring tools can fill the gaps when it comes to monitoring and observing the applications and systems they connect with.

There’s a balance to ensure success with your automation initiatives by way of building a toolbox of technologies that provide a comprehensive set of capabilities to align with the needs of business and IT.

5 Recommendations to Ensure Success of Intelligent Automation Initiatives

To ensure the success of intelligent automation initiatives, there are 5 recommendations that will support the operational experience in an enterprise.

#1: Fortify Integration of AI and Automation

AI-driven automation is moving faster than IT security can keep up with. It’s not uncommon to find enterprises having already deployed thousands of RPA bots across an enterprise. And now here comes the next big wave of innovation around Generative AI destined to touch every application within an organization.

Now is the time for enterprises to fortify the oversight and monitoring of AI automation tools whether it is RPA, IDP, BPM, ECM, Chatbots, and so on. It is common that these intelligent automation tools will connect to each other and share sensitive data within the process as part of a solution. If not properly managed with the right application monitoring and observability tools, outages within critical processes will happen, company data will be put at risk, and goals of scaling intelligent automation will be lost.

#2: Secure your bots

The “bot” is software that automates repetitive tasks and is referred to as robotic process automation (RPA). It is a key technology piece to every intelligent automation platform given it can interface with virtually any system – modern web-based, legacy, and desktop – and automate any simple repetitive task that a human performs.  

Like your employees, RPA bots require full accountability for their actions. There must be a complete audit trail of all actions that go beyond simply storing and reviewing the raw log data. The potential risk of bots making mistakes, bots being granted new access rights, or even employees failing to properly secure bot is a real problem. If a threat actor identifies an RPA farm with thousands of digital workers, the temptation to access RPA management consoles is immense!

#3: Bolster your operational oversight of intelligent automation

The need to bolster your operational oversight of intelligent automation requires that you provide monitoring and security views into operations that benefit the key stakeholders including IT operations, security, and Center of Excellence automation teams.

To monitor the success of your intelligent automation initiatives, you must have the right data views into your use of the technology (e.g., RPA, IDP) and the systems (ERP, CRM, ECM) that are core to your automated processes.

There are key questions various personas within your organization have, and in some cases, clear action must be taken. It is important that monitoring tools not only observe the performance of the applications and systems they interact with but also provide intelligent insight into the security of all automated activities.

The needs of different teams will be different. For the Center of Excellence automation owners, the insight they require is directly connected to processes running smoothly and taking corrective action quickly when failures occur. They seek answers to questions like:

  • How many bots do we have running and are any bots reporting errors?
  • Are tasks building up in queues for unknown reasons?
  • Are we able to scale our automation?

In the case of IT security operations, they want to know…

  • Have any bots been compromised by those who have permission to create or modify the bot design?
  • Can we do a full audit trail of the work these bots do?
  • Have permissions levels for a bot been changed or granted access to new systems?

It is important in your planning to make sure you have the proper operational oversight that satisfies all teams and the answers to the questions they are asking.

#4: Adopt data-driven security decision making

The need to access data from logs, systems, and process data provides the source of information that can be correlated and surfaced within a monitoring and observability tool. This allows process owners, IT operations, and security to find and address any issue quickly. Incident intelligence views are formed based on raw audit log data that is further analyzed and triggers alerts to the right people.

A full audit trail of AI bot activity, human interactions, and other AI data-driven services must be observed to take proactive security measures before a threat or unauthorized bot or human activity becomes a major problem.

Take, for example, an enterprise that has several thousand automated processes using bots connected with AI Services that process unstructured data from a document. Over time, processes will change, bots will get updated, or the training of AI document models will be refreshed. These types of configuration changes and automated updates can create blind spots related to the technology assets, users who are interacting within the process, and owners within tools like RPA that have user permission to make administration-level changes.

It is important you leverage all the available data to maintain centralized visibility to different teams, helping to ensure sensitive information remains protected across the technology stack and meets your compliance and security goals.

#5: Never stop. Always adapt and enhance security.

Enterprises are continuing to leverage AI automation across all facets of the business, and with the expanded use comes more complexity with providing insight and observability of the applications and processes involved in AI automation.

IT operations and security teams are constantly adapting to new security threats and should always adapt and enhance the security around bots, people, and the use of AI technology that is powering your applications and processes. With the increasing use of Generative AI, application monitoring tools will become even more important to provide the necessary oversight to AI-driven processes and systems that AI bots are accessing and interacting with.

Next Steps

Intelligent automation is pushing an organization’s operations to evolve, enhance efficiency, and deliver a compelling customer experience. However, the successful rollout of automation projects must cover the operational experience internally with extensive monitoring and oversight after implementation is required.

It is important to prioritize security across all the intelligent automation tools and systems they connect with for a safe and prosperous future around your digital transformation journey. This will ensure the operational experience within your organization supports the efforts to reduce risks and avoid projects from being quickly pulled back after implementation. The operational experience (behind the firewall) should at least equate to the customer experience (outside the firewall) or your overall total experience will not rise enough to deliver the expected business impact.

Furthermore, If your company uses one of the many IDP or RPA tools from the likes of UiPath, Hyland, Kofax, Microsoft, or others, there’s a solution from Reveille that can help your IT operations, security, and automation teams feel confident that you have pulled out all the stops to observe and monitor the performance and security of the AI applications and systems.