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

5W1H Analysis

Who

The key stakeholders involved include technology organisations, AI developers, cybersecurity experts, and enterprises adopting generative AI technologies. Notable organisations are likely to be technology leaders and AI governance bodies.

What

The focus is on understanding and mitigating the "confused deputy problem" in multi-agent generative AI systems. This issue arises when an AI acts on behalf of multiple entities and inadvertently causes security or operational risks.

When

The analysis is pertinent from May 2025, reflecting ongoing AI development trends and risk management discussions over the past and upcoming months.

Where

The discussion is globally relevant, affecting markets where AI and advanced technology systems are being actively developed and implemented, particularly in North America, Europe, and Asia.

Why

As AI models become increasingly autonomous, addressing security vulnerabilities like the confused deputy problem is crucial for maintaining trust and ensuring safe AI deployment across industries.

How

Methods include revising risk management frameworks, adopting advanced AI governance protocols, and implementing robust security measures to prevent AI systems from inadvertently acting against their intended purpose.

News Summary

The article highlights the need for organisations to rethink risk management strategies in the context of developing and deploying multi-agent generative AI systems. The confused deputy problem, a security issue where an AI interfaces between entities, is a pressing concern that could lead to unintended consequences. Organisations must prioritise robust AI governance mechanisms to prevent such risks.

6-Month Context Analysis

In the past six months, there has been a growing focus on AI safety and ethics, particularly around governance and risk management. Initiatives by international bodies to standardise AI ethics and recent AI-driven security breaches in different parts of the world highlight the continuing relevance of addressing AI vulnerabilities. This reflects an industry-wide recognition of the need for more sophisticated risk management in AI deployments.

Future Trend Analysis

The emerging trend is the increasing focus on AI governance, ethics, and risk management frameworks, driven by the complexities and capabilities of multi-agent AI systems.

12-Month Outlook

Over the next 12 months, we can expect stricter AI governance protocols and the development of industry standards for AI risk management. Organisations will likely invest in training AI ethics professionals and developing tools to mitigate risks associated with agentic AI.

Key Indicators to Monitor

- Regulatory changes in AI governance - Increased investment in AI risk management solutions - Development of industry-standard protocols on AI ethics and safety - Partnership announcements between tech companies and governance bodies

Scenario Analysis

Best Case Scenario

Organisations adopt comprehensive AI risk management frameworks, significantly reducing vulnerabilities. This leads to increased trust in AI systems and seamless integration into various industries, fostering innovation and economic growth.

Most Likely Scenario

While some organisations will implement effective governance measures, others may lag, resulting in a mix of secure and vulnerable AI systems across industries. This could maintain a moderate risk level until uniform standards are widely adopted.

Worst Case Scenario

Failure to address the confused deputy problem and other vulnerabilities could lead to significant AI-related incidents. This might result in stricter regulations, slowing down AI innovation and adoption due to heightened security concerns.

Strategic Implications

Organisations must prioritise AI security and governance to mitigate risks associated with agentic AI. Investing in AI ethics training, collaborating with industry bodies to develop standards, and continuously updating risk management frameworks are crucial steps.

Key Takeaways

  • Organisations should address the confused deputy problem in AI to prevent security risks.
  • Enhanced AI governance protocols are critical for maintaining trust in AI systems.
  • Global markets need to adopt uniform AI risk management standards.
  • Monitoring regulatory changes can guide strategic adjustments in AI development.
  • Collaborations with industry bodies can expedite the creation of effective governance frameworks.

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