The evolution of AI security: from chatbots to agentic ecosystems

The mental model of many cybersecurity teams - a user interacting with a chatbot, which in turn communicates with a language model - is now outdated. Modern AI applications have surpassed this simple architecture, becoming complex ecosystems with autonomous agents, MCP servers, and orchestration tools.

What makes agentic AI security unique

Agentic AI security focuses on protecting applications that perform actions, not just those that answer questions. It covers the entire AI execution path: from agents making decisions to the tools and APIs they invoke, from MCP servers connecting them to real systems to documents retrieved in context and orchestration layers managing workflows between agents.

The expansion of the attack surface

Three fundamental changes have redefined the AI threat landscape:

  • Agents now perform autonomous actions, turning a response error into a potentially harmful operational incident
  • The arrival of the Model Context Protocol and similar tool integration schemes has expanded the attack surface
  • Interactions have multiplied, with internal communications between sub-agents, external calls to MCP servers, and retrievals from knowledge repositories that never touch the chat window

The challenge of visibility

The crucial question for organizations is: would you know what your agents just did? In most cases, the answer is no. This is not because security teams are behind, but because the visibility layer for this architecture is still under development.

Towards complete AI execution path security

The discipline of AI application security is evolving towards protecting the entire execution path: discovery of all AI assets in the environment, understanding relationships between agents, models, and tools, and applying policies on critical interactions.

The main threats to agentic AI security

According to the future OWASP Top 10 for Agentic Applications (2026), the main risks include:

  • Agent goal hijacking
  • Tool abuse and exploitation
  • Identity and privilege abuse
  • Agentic supply chain vulnerabilities
  • Unexpected code execution
  • Memory bleed and context poisoning
  • Insecure inter-agent communication
  • Cascading failures
  • Human-agent trust exploitation
  • Rogue agents

Security tools for agentic AIs

Thales Imperva AI Application Security already protects applications supported by LLM today. The company announced in December 2025 the AI Security Fabric, which includes an MCP security gateway and runtime access control for agentic AI interactions in the roadmap for 2026.

Implications for corporate cybersecurity

This evolution requires a complete rethink of cybersecurity strategies. Organizations that first update their mental model will have a significant advantage in protecting their AI infrastructures.

Where to find additional information

To delve deeper into the topic, you can consult the upcoming white paper "Beyond the LLM Top 10" or visit the page dedicated to the Imperva AI Application Security.

The evolving regulatory landscape

The emergence of agentic AIs is pushing legislators to review existing regulatory frameworks. The European Commission is working on an update to the AI Act that includes specific provisions for agentic applications, while in the United States the National Institute of Standards and Technology (NIST) is developing guidelines for risk assessment in these advanced systems.

The impact on the liability model

The decision-making autonomy of agents raises new legal questions about liability for errors. Tech-savvy lawyers predict an increase in lawsuits related to "rogue agents" operating outside of intended parameters, forcing companies to review their service contracts and liability waivers.

The challenges for development teams

Developers are facing unprecedented complexity. "We need to move from a prompt-based approach to one that considers the entire ecosystem of interactions," says a Thales engineer. "Every new integrated tool is a potential attack vector that must be evaluated and protected."

The critical role of zero trust architectures

Experts agree that zero trust architectures will be fundamental for agentic AI security. "Every interaction between agents, tools, and external systems must be authenticated, authorized, and encrypted," explains a security analyst. "Implicit trust is the number one enemy in this new paradigm."

The importance of continuous training

Organizations are heavily investing in training programs for security and development teams. "We need to bridge the skills gap between traditional cybersecurity knowledge and the new challenges posed by agentic AIs," says a training manager at a leading company in the sector.

The implications for the supply chain

The complexity of agentic interactions is making supply chains more vulnerable. "An attack on an apparently marginal component can have cascading effects on the entire system," warns a supply chain security expert. "It is essential to adopt a holistic approach to risk assessment."

The future of agentic AI security

According to analysts, by 2030 the agentic AI security market could be worth over $20 billion, with an annual growth of 40%. Companies that invest today in advanced security solutions will be better positioned to face tomorrow's challenges.

The evolution of security frameworks

Traditional application security frameworks, such as OWASP, are updating their guidelines to include the specific needs of agentic AIs. The new OWASP Top 10 for Agentic Applications, scheduled for 2026, will provide a detailed roadmap for mitigating emerging risks.

The ethical challenges

In addition to security concerns, agentic AIs raise important ethical questions. "We need to ensure that agents operate transparently and responsibly," says an AI ethics expert. "The ability to explain the decisions and behaviors of agents will be crucial to maintaining public trust."

The importance of cross-sector collaboration

Experts emphasize the need for closer collaboration between companies, governments, and academics. "Only by working together can we develop effective solutions and common standards for agentic AI security," concludes a representative of an international organization.

Towards proactive security

Agentic AI security requires a proactive approach. Organizations must be ready to quickly adapt to new risks and threats. "It's not just about defending against attacks, but building resilient systems from the start," says a cybersecurity expert.

Resources for further exploration

For those who wish to explore the topic further, several resources are available:

  • The upcoming white paper "Beyond the LLM Top 10"
  • The article "MCP Server Security: The Blind Spot in Your AI Stack" (https://www.imperva.com/blog/mcp-server-security-blind-spot-ai-stack/)
  • The page dedicated to the Imperva AI Application Security

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