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

5W1H Analysis

Who

Key stakeholders include artificial intelligence researchers, technology companies, and organisational leaders preparing to implement multi-agent generative AI systems.

What

The news focuses on the need for organisations to reconsider risk management strategies in light of potential issues stemming from deploying agentic AI, specifically the "confused deputy problem."

When

The discussion is centred around current and ongoing developments in AI technology, as highlighted in the blog published on 19th May 2025.

Where

This issue pertains globally, affecting markets wherever AI technologies are being developed and deployed, particularly in regions leading in AI innovation.

Why

The primary reason for addressing this problem is to mitigate risks associated with the deployment of multi-agent generative AI systems, ensuring that these AI agents perform tasks as intended without unintended interference or misdirection.

How

Organisations are urged to adopt new risk management frameworks and understand the complexities of AI ecosystems better to prevent the confused deputy problem, which arises when a system component incorrectly uses its authority on behalf of another.

News Summary

In the context of advancing agentic AI technologies, organisations are encouraged to revise their approach to risk management, addressing challenges like the confused deputy problem. This involves understanding the interplay between multi-agent systems and ensuring AI systems operate as intended without misapplying authority among components.

6-Month Context Analysis

Over the past six months, there has been a marked increase in the deployment of AI systems across various sectors, necessitating robust discussions on AI governance and security frameworks. This period has seen collaborations among tech developers to establish best practices to minimise AI system risks, indicating a heightened focus on AI safety and ethics.

Future Trend Analysis

The news reflects a growing trend towards enhanced AI safety protocols and the integration of comprehensive security measures within AI deployments. The focus is shifting from mere functionality to the ethical deployment of intelligent systems.

12-Month Outlook

It is expected that technology companies will prioritise developing AI systems with built-in governance frameworks. Organisations may increasingly invest in AI risk management tools, leading to an uptick in collaborations between AI developers and governance experts.

Key Indicators to Monitor

- Adoption rate of governance frameworks among AI developers - Instances of AI-related system failures or security breaches - Regulatory developments and standards on AI deployment

Scenario Analysis

Best Case Scenario

Organisations successfully implement robust AI governance frameworks, leading to seamless integration of multi-agent systems that enhance operational efficiency without security breaches.

Most Likely Scenario

Incremental improvements in AI risk management practices are observed, as more organisations gradually adopt new frameworks, though challenges in comprehensive implementation remain.

Worst Case Scenario

Failure to address risks associated with multi-agent generative AI could result in significant system failures, leading to operational disruptions and a loss of stakeholder trust.

Strategic Implications

Organisations should proactively engage with AI experts to understand potential risks and develop in-house expertise in AI governance. Collaboration across industries will be essential to develop and adopt comprehensive risk management strategies effectively.

Key Takeaways

  • Organisations must engage AI researchers and experts (Who) to update risk management strategies (What).
  • Understanding AI ecosystems (How) is critical to prevent risks like the confused deputy problem.
  • Global markets (Where) should stay informed about developments in AI safety protocols.
  • Businesses should monitor regulatory changes (What) in AI to remain compliant.
  • Building AI systems with native governance frameworks can reduce risks (Why).

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