Python RPA Security – Check out best practices

➡️ What about security in Python RPA? Check out good practices to make your robots more secure 🤖👇

📌 In RPA, bots often have direct communication with critical systems that, without proper measures, can leave sensitive data vulnerable and facilitate unauthorized access;

💡 The most prominent tech companies use Python massively in their stacks. With Python, your RPA operation can guarantee point-to-point the highest level of security (with encryption, vulnerability testing, and more);

💡 See BotCity ‘s recommendations for further protecting your bots and mitigating key risks (according to OWASP):

✅ Protect the environment in which the bot is developed and run. Including the operating system, database, dependencies, and network, with the latest security updates;

✅ Separate development, test/homologation, and production environments;

✅ Make communication secure with VPNs, encryptions, SSL/HTTPS certificates, allow-lists, and specific ports on the firewall;

✅ Ensure that actions are assigned with secure and unique identification credentials for each robot;

✅ Separate credentials from the code and store them in an encrypted vault, and change them periodically;

✅ Grant strictly necessary permissions to the bot and collaborators, with multi-factor authentication where applicable;

✅ When deactivating a bot, don’t forget to revoke the credentials;

✅ Monitor the activities of bots and create alerts for unexpected behavior;

✅ Manage sessions with screenshots or videos;

✅ Protect the integrity of logs by storing them separately and ensuring that they are complete and auditable;

✅ Do not use the default Python interpreter. It is likely outdated. Install and make sure you have the latest version;

✅ Use virtual environments;

✅ Avoid making relative imports;

✅ Beware of unknown, unofficial, or out-of-date libraries (check the Snyk Advisor). Be careful not to misspell the library name and use a malicious one by mistake;

✅ Avoid loading unnecessary data/libraries;

✅ Keep the code clean;

✅ Always validate/treat data entries to protect against injections, use encryption for sensitive data, and handle errors and exceptions;

✅ Avoid leaving unlimited things and free up memory for objects you are no longer going to use;

✅Set DEBUG = FALSE in production;

✅ Deserialize with caution when using Pickle. Evaluate using PyYAML;

✅ Establish an adaptive governance framework;

✅ Perform unit tests, integration tests, canary releases, and periodically evaluate the implementation as a whole;

✅ Make code review, versioning control, and backups;

✅ Scan your code for vulnerabilities (get to know Bandit, Snyk Code, SonarQube…);

✅ Reflect on scenarios where things can go wrong and have a plan for incidents;

✅ Have a proactive dialog with the security team from the beginning of the project;

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