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Evaluating bot security in financial process automation

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Financial process automation is the use of artificial intelligence (AI) to perform various tasks that would otherwise require human intervention, such as data entry, invoice processing, reconciliation, reporting and more. By automating these tasks, businesses can save time, reduce errors, improve efficiency and enhance customer satisfaction.

However, automation also comes with its own set of challenges and risks, especially when it comes to security. The bots that execute the tasks on behalf of or assuming the role of a human user need to be carefully designed, monitored and controlled. A SaaS-based automation solution, must implement a zero-trust environment, where the bots are also treated just like human users, for the very reason that the bots assume the role of a human user for executing the tasks.

What is zero-trust security?

Zero-trust security is a principle that assumes that no entity, whether internal or external, is trustworthy by default. It requires verifying the identity and permissions of every user and device before granting access to any resource or data. It also requires monitoring and auditing all activities and transactions to detect and prevent any malicious or unauthorized behavior.

Zero-trust security is especially important for financial process automation, as it involves sensitive and confidential data that needs to be protected from cyber attacks, data breaches, fraud and compliance violations. By applying zero-trust security, the bots are provided with just enough permissions to perform their tasks, and that they are not compromised or misused by hackers or rogue employees.

How zero-trust security principles help secure the bots?

Here are a few ways in which zero-trust security principles help secure the bots in financial process automation:

Using strong authentication and authorization mechanisms for the bots. The automation platform must verify the identity and permissions of the bots before allowing them to access any resource or data. The platform must identify a bot executing tasks for a customer organization from other bots executing tasks for different customer organizations. This is very critical in case of Multi-Tenant SaaS based models. 

Implement least-privilege principle for your bots. This means that the bots are granted only the minimum level of access and permissions that they need to perform their tasks, and nothing more. This way, the bots are prevented from accessing data that is beyond the permissible boundaries and also limit the potential damage that a compromised or misused bot can cause.

Track and audit various activities of the bots. It is very critical to log and continuously monitor all the actions and transactions that the bots perform, such as what data they access, modify or delete, what systems they interact with, what errors or exceptions they encounter and so on. These logs need to be reviewed regularly using analytics tools to identify anomalies and suspicious patterns that may indicate a security breach or a compliance violation.

Conclusion

Organizations that look to optimize their financial processes through AI-driven SaaS automation solutions should evaluate the solutions paying special attention to the security aspects governing bots, and on how their organization’s data and critical digital assets are secured using security principles such as zero-trust.

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