Before you build agentic AI, understand the confused deputy problem Analysis Report

5W1H Analysis

Who

Key stakeholders include AI developers, software engineers, cybersecurity experts, and organisations aiming to implement multi-agent generative AI systems.

What

The focus is on understanding the "confused deputy problem" in the context of developing agentic AI systems, which are generative AI systems capable of autonomous decision-making.

When

The discussion and analysis were highlighted as of 19th May 2025, with ongoing implications for future AI developments.

Where

The implications are global, affecting any organisation or market pursuing advanced AI technologies, particularly with high traction in tech-centric regions.

Why

The need to understand the confused deputy problem arises from its impact on risk management and security within AI systems that operate autonomously and interact with other systems.

How

Organisations are encouraged to employ rigorous risk assessment frameworks and develop robust validation mechanisms to mitigate security risks associated with agentic AI.

News Summary

The news focuses on highlighting the importance of understanding the "confused deputy problem" as organisations prepare to integrate multi-agent generative AI into their operations. This problem represents a significant security risk, wherein an AI agent might misuse permissions, leading to potential vulnerabilities. The development and deployment of such systems require new approaches to risk management, necessitating comprehensive security assessments and robust control frameworks.

6-Month Context Analysis

Over the past six months, there has been a burgeoning interest in autonomous AI systems, with numerous conferences and publications addressing AI safety and risk. Noteworthy is the increase in collaboration between tech companies and academia to address AI risks, including workshops specifically targeting the confused deputy problem and related security concerns.

Future Trend Analysis

The emphasis on AI safety will likely lead to a surge in innovations around AI security protocols, especially for systems with high autonomy.

12-Month Outlook

In the next 12 months, expect a proliferation of AI risk management software and enhanced collaboration between tech firms to reduce AI vulnerabilities. Regulatory bodies may also introduce guidelines specific to AI risk assessments.

Key Indicators to Monitor

- The number of AI conferences addressing security issues - Publication frequency of AI risk management frameworks - Incidents of AI-related security breaches reported by major firms

Scenario Analysis

Best Case Scenario

Advancements in AI security protocols lead to the safe and widespread adoption of multi-agent systems, facilitating innovation and efficiency across industries.

Most Likely Scenario

Organisations adopt a cautious approach, integrating AI with comprehensive safeguards, resulting in moderate growth of agency-driven AI systems with minimal incidents.

Worst Case Scenario

Failure to adequately address the confused deputy problem leads to significant AI security breaches, resulting in economic and reputational damage for corporations involved.

Strategic Implications

Organisations must prioritise the development of stringent security frameworks tailored to agentic AI, invest in ongoing employee training about AI risks, and participate in cross-industry collaborations to establish best practices.

Key Takeaways

  • Organisations globally must reassess security approaches when dealing with agentic AI systems.
  • Risk management frameworks need urgent updates to incorporate AI-specific vulnerabilities.
  • Collaborative efforts between tech companies and academic bodies are crucial for understanding AI threats.
  • The next year is pivotal for AI security innovations and regulation development.
  • Monitoring AI security breaches will be critical in anticipating industry challenges.

Source: Before you build agentic AI, understand the confused deputy problem