IAM for Robotic Process Automation: How to avoid Security Challenges

Robotic Process Automation (RPA) is positioned between manual, GUI-driven workflows executed by humans and fully automated processes built on APIs. It addresses scenarios where automation through APIs or conventional coding is impractical, either because adequate APIs are missing or because the work depends on human understanding, judgment, and decision-making. Two primary use cases emerge: a simpler one focused on automating repetitive manual tasks such as data entry and data extraction from GUIs using screen scraping, often paired with OCR to read scanned documents; and a more complex one that integrates AI to augment or replace human interpretation and decision-making, such as analyzing text, preparing decisions, and accelerating knowledge-intensive processes.

RPA introduces “robot identities” that must be authenticated and authorized like any other digital identity, requiring accounts and entitlements across target systems. The central IAM risk is not inherently the existence of robots, but poor governance—especially the tendency to create “super robots” by assigning one robot to an entire business process or reusing a single functional account across multiple processes for convenience. This aggregates entitlements, violates least privilege, expands attack surface, and leads to “RPA done wrong.” Instead, robot accounts should be defined per business activity, typically requiring fewer permissions than the humans they replace.

Managing robot identities can rely on standard IAM capabilities: IGA for lifecycle management, access requests, approvals, and reviews; and PAM for robot authentication constraints via password rotation and application-to-application password management or certificate-based approaches. Ownership, onboarding, and mover/leaver processes must explicitly cover robots, with clear accountability. Monitoring (including UBA) and strong API security further reduce risk, particularly as AI-enabled RPA tends to require broader access and benefits from segregating activities into multiple constrained robot accounts.

• RPA can automate data entry into GUIs through screen scraping when adequate APIs are not available.
• OCR is used in some RPA implementations to read scanned documents and enter extracted data automatically.
• Robots require digital identities that must be authenticated and authorized through accounts and entitlements.
• A common anti-pattern is creating “super robots” that accumulate entitlements from multiple former human tasks.
• Using a single technical or functional account for robots across different business processes occurs for the sake of simplicity.
• Robot accounts defined per business activity can require fewer entitlements than the humans who previously performed the tasks.
• Robots cannot use biometrics, cannot readily handle knowledge-based authentication, and cannot use physical OTP tokens or smartcards.
• Password-based robot authentication can be supported through password rotation and application-to-application password management.
• Certificate-based authentication is an alternative if certificates are secured and rotated regularly.
• AI-supported RPA use cases include accelerating underwriting decisions, improving KYC document analysis, and pre-filling credit scoring documents.

• “The most important advice is to avoid ‘super robots’.”
• “Robots are a logical target for attackers.”
• “Reduce the attack surface by focusing robots on granular business activities, not complete business processes.”
• “There needs to be a well-thought-out approach for managing such robot identities.”
• “Oversimplifying the management of robots is not good practice and typically results in RPA being ‘done wrong’.”
• “Robots must be managed by the IGA tool.”
• “There needs to be a Lifecycle Management approach for such robots.”
• “Authentication of robots can become a specific issue, which needs to be understood and solved.”
• “A specific challenge of IAM for RPA is authentication.”
• “In contrast to the initial focus of RPA on automating repetitive human tasks, the second area where RPA is gaining ground is in decision support and decision making.”