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

5W1H Analysis

Who

Key stakeholders in this development include organisations developing multi-agent generative AI solutions and cybersecurity experts addressing related risks.

What

The article addresses the need for organisations to rethink risk management strategies concerning the confused deputy problem in the context of agentic AI development.

When

The discussion is currently relevant and aligns with ongoing developments as of May 2025.

Where

The article primarily pertains to global markets with a strong presence in AI technology development, including North America, Europe, and Asia.

Why

The emphasis on understanding the confused deputy problem stems from the potential for security vulnerabilities in AI systems that employ autonomous agents without proper oversight and protocol adjustments.

How

The considerations involve re-evaluating traditional risk assessments and implementing robust security frameworks to mitigate the confused deputy problem in multi-agent AI systems.

News Summary

Organisations developing multi-agent generative AI need to reassess their risk management strategies by understanding the confused deputy problem. The focus is on preventing security vulnerabilities as autonomous AI systems become more prevalent. This involves addressing how AI agents can be manipulated or fail due to mismanagement of authority, thereby exposing systems to potential risks.

6-Month Context Analysis

In the past six months, there has been an increasing focus on AI security, as seen with the rise of concerns surrounding AI ethics and safety. Companies and tech giants have been hosting conferences and publishing research addressing AI's potential risks, emphasising the need for updated security protocols in AI systems.

Future Trend Analysis

The drive towards more secure AI systems will likely intensify as developers and users demand clarity and safety features to navigate AI's complexities.

12-Month Outlook

In the next 6-12 months, we can expect tighter integration of AI safety measures, broader industry standards on AI security, and possibly regulatory frameworks emerging to manage AI risks more effectively.

Key Indicators to Monitor

- Adoption rates of new AI security protocols - Number of reported AI-related security breaches - Implementation of global AI security regulations

Scenario Analysis

Best Case Scenario

AI systems become increasingly secure, leading to booms in AI-driven innovation with minimal security incidents, fostering public and corporate trust in technology.

Most Likely Scenario

Steady progress in developing security frameworks, with gradual adoption of risk management practices, mitigates but does not entirely eliminate security concerns.

Worst Case Scenario

Significant breaches due to emerging issues in AI systems result in public distrust, urgent regulatory crackdowns, and stunted progress in AI development.

Strategic Implications

Organisations must prioritise building comprehensive AI security and risk management frameworks, working with cybersecurity experts to regularly reassess risks and vulnerabilities. Emphasising secure development practices and transparency with stakeholders will be crucial.

Key Takeaways

  • Organisations involved in AI development must address the confused deputy problem to secure AI systems.
  • Understanding past security breaches can inform better risk management practices for AI.
  • Global markets investing in AI must anticipate and adapt to regulatory changes affecting AI security standards.
  • Infrastructure for AI needs robust security analysis tools to evaluate AI activities and potential vulnerabilities.
  • Stakeholders should prioritise transparency and proactive communication about AI risks and safety practices.

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