Generative AI, powered by models like ChatGPT, is reshaping technology and business. But with its potential comes risk. According to the Thales Data Threat Report 2025, nearly 70% of organizations consider the rapid pace of these technologies the greatest security concern.
In this article, we’ll explore the main risks—especially when running Python scripts on local machines without supervision—and show how BotCity offers a secure, scalable solution.
Main Risks of Generative AI
Here are the key risks of Generative Artificial Intelligence (AI):
A rapidly evolving ecosystem
The fast adoption of generative AI increases the risk of unidentified vulnerabilities. Models evolve frequently, and security measures don’t always keep up.
Integrity and trust breaches
Over 64% of companies report concerns about data integrity when using AI.
Adversarial attacks and data poisoning
AI models can be compromised by manipulated data, jeopardizing automated decision-making.
Sensitive data leaks
When AI generations run on non-isolated local machines, data may be exposed without proper monitoring.
Regulatory violations
The report highlights the high risks of adopting AI without governance. While 73% of companies invest in AI-specific security, current measures remain insufficient.
Running Python Scripts on Local Machines
Lack of visibility
Running scripts directly on PCs without centralized logging makes it harder to detect failures or unauthorized access.
No segregation
Mixing modeling/prototyping with production execution creates vulnerabilities, enabling unauthorized access.
Unmapped risks
Local environments don’t follow the strict security standards of data centers, such as strong access controls, encryption, and continuous monitoring.
Why Is It Important to Understand the Risks of Generative AI?
Data tracking
The Thales Data Threat Report reveals that 24% of organizations are unsure where their data is stored — which increases the risk of data leaks.
Irreversible impact
Sensitive data exposed through a poorly configured script can damage a company’s reputation and lead to legal implications.
How to mitigate these risks?
Now that you understand the main risks of generative AI, here’s how to address them:
Runtime distancing (environment isolation)
Create dedicated environments—such as virtual machines or containers—to run AI models and Python scripts. This ensures clear separation between development and production.
Learn more: What is a virtual machine and what are its advantages?
Centralized orchestration
Use platforms that enable scheduling, controlled execution, logging, and monitoring — eliminating scattered and isolated script runs.
Robust security and governance
Implement dependency control policies, automated vulnerability scanning, frequent code audits, and access permission tracking.
BotCity as an Integrated Solution to AI Risks
BotCity provides a complete platform for Python and AI automation, directly addressing the highlighted risks:
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Isolated and standardized environments: bots run in containers with full dependency control.
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Efficient orchestration: scheduling, centralized logs, and continuous monitoring.
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Built-in governance: access restrictions, security checks, and certified libraries.
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Flexibility with security: bots can leverage AI APIs without compromising organizational integrity.
Did You Understand the Risks of Generative AI?
Generative AI brings immense opportunities but also significant risks — especially when combined with uncontrolled local execution of Python scripts.
BotCity delivers a robust, practical solution that combines advanced automation with strong protection and centralized governance, ready to scale AI securely within enterprises.
If you use Python to integrate AI into your bots and need a secure, scalable solution, create a free BotCity account now and safeguard your automations!
