{"id":29824,"date":"2025-09-23T17:00:47","date_gmt":"2025-09-23T20:00:47","guid":{"rendered":"https:\/\/nocodestartup.io\/?p=29824"},"modified":"2025-09-23T17:00:49","modified_gmt":"2025-09-23T20:00:49","slug":"security-in-ai-agents","status":"publish","type":"post","link":"https:\/\/nocodestartup.io\/en\/security-in-ai-agents\/","title":{"rendered":"Security in AI Agents: Strategies to protect intelligent workflows with confidence and robustness."},"content":{"rendered":"

Security in AI agents has become a strategic priority for companies implementing autonomous workflows in critical sectors such as finance, legal, customer service, and operations.

With the increasing use of generative artificial intelligence and agents that perform tasks without human supervision, ensuring data security, legal compliance, and the integrity of decisions has become vital.<\/p>\n\n\n\n

\"What
What is security in AI agents?<\/figcaption><\/figure>\n\n\n\n

What is security in AI agents?<\/strong><\/h2>\n\n\n\n

Security in AI agents is the set of best practices, technologies, and policies designed to protect autonomous agents against failures, cyberattacks, and misuse of sensitive information.

This includes measures such as prompt validation, call authentication, dynamic access control, log monitoring, and auditing of automated decisions.<\/p>\n\n\n\n

This concern is especially relevant in highly regulated business environments, such as banks, insurance companies, and technology companies, where integration with APIs<\/a> Internal and legacy systems require a more rigorous level of governance.<\/p>\n\n\n\n

AI security versus AI protection<\/strong><\/h2>\n\n\n\n

Although the terms "AI security" and "AI protection" are often used synonymously, they represent different and complementary concepts.

THE AI security<\/strong> It relates to how we build, monitor, and align models so that their results conform to human values, avoiding unintended or unwanted consequences.

Already the AI protection<\/strong> This refers to defending these systems against external threats, such as cyberattacks, data leaks, and unauthorized access.<\/p>\n\n\n\n

Understanding this distinction is crucial for professionals who manage workflows with autonomous agents in corporate environments.

AI security is connected to the ethical and technical alignment of the agent with the organization's objectives, while AI protection relies on access policies, encryption, network segmentation, and cybersecurity practices.<\/p>\n\n\n\n

These two layers \u2014 internal security (alignment, explainability, robustness) and external protection (firewalls, tokens, audits) \u2014 should be viewed as interdependent parts of a resilient enterprise AI architecture.<\/p>\n\n\n\n

Most common risks for independent agents<\/strong><\/h2>\n\n\n\n

Among the main risk vectors are prompt injection attacks, where malicious commands are disguised as input devices in input fields, redirecting the behavior of the agents.

According to OWASP LLM Top 10<\/a>, Prompt injection is currently one of the most exploited vulnerabilities in generative AI applications.<\/p>\n\n\n\n

Another critical risk is the leakage of sensitive data during interactions with agents that lack proper encryption or sandboxing, especially in workflows involving internal documents or integrations with ERP systems.<\/p>\n\n\n\n

\"How
How to apply security to workflows with AI agents.<\/figcaption><\/figure>\n\n\n\n

How to apply security to workflows with AI agents.<\/strong><\/h2>\n\n\n\n

The first layer of protection involves creating a model of AI governance<\/strong>, as proposed by NIST AI Risk Management Framework<\/a>, which organizes risks into categories such as reputational damage, loss of operational control, and privacy violations.<\/p>\n\n\n\n

In practice, there are several ways to mitigate threats. One of the most effective resources is the adoption of a Zero Trust architecture, as exemplified by... Cisco<\/a>, in which each action of the agent needs to be verified by context, identity, and permission.<\/p>\n\n\n\n

Tools like watsonx.governance<\/strong> and Azure AI Security Layers<\/strong> They have implemented solutions to allow these agents to operate with their own digital identity, creating "identity cards" with OAuth2 authentication and traceable logs.<\/p>\n\n\n\n

Recommended tools and frameworks<\/strong><\/h2>\n\n\n\n

At No Code Start Up, we recommend using platforms such as:<\/p>\n\n\n\n