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

5W1H Analysis

Who

Key stakeholders include organisations developing multi-agent generative AI systems, software developers, risk management professionals, and companies like Hashicorp which contribute to AI research and security solutions.

What

The article discusses the "confused deputy problem" in the context of building agentic AI systems. It offers insights into the evolving perception and management of risk associated with multi-agent generative AI technologies.

When

The discussion is part of ongoing conversations around AI development, particularly from 2023 to 2025, with the article published on 19th May 2025.

Where

While the principles apply globally, companies in technologically advanced markets such as North America and Europe are expected to take the lead.

Why

Addressing the confused deputy problem is crucial as AI systems become more autonomous and integrated across industries. This challenge involves ensuring that security protocols in AI systems do not unintentionally give undue authority to processes that misuse or misinterpret data.

How

Organisations need to shift their risk management approaches, incorporating enhanced security protocols and regulatory frameworks. This involves understanding the potential for AI models to act beyond their intended capabilities and creating safeguards against such behaviours.

News Summary

Organisations must rethink traditional risk models when preparing for the deployment of multi-agent generative AI technologies. A critical issue, the "confused deputy problem," highlights the need for advanced security measures to regulate AI behaviours. Focused primarily on tech-heavy regions such as North America and Europe, the development of robust AI systems necessitates a deeper understanding of the ways AI might deviate from expected outcomes, driven by the potential autonomy these systems are granted.

6-Month Context Analysis

Over the past six months, there has been an increasing focus on AI ethics and security. Major AI players have been involved in discussions about AI transparency and accountability. Conferences dedicated to AI, like NeurIPS and CES, have highlighted the importance of addressing systemic risks related to AI delegation and autonomy, reflecting a growing awareness among AI developers and regulators.

Future Trend Analysis

- Growing reliance on AI in decision-making across major industries. - Increased regulatory scrutiny on AI systems to prevent misuse. - Development of AI-specific security frameworks.

12-Month Outlook

Expect significant advancements in AI security solutions and related software, with organisations prioritising ethical AI practices. Regulatory bodies might introduce new guidelines to ensure AI systems operate within safe boundaries, which could impact AI deployment timelines and budgets.

Key Indicators to Monitor

- Legislative developments related to AI security. - New partnerships between AI developers and security firms. - Reports of AI-related security breaches or malfunctions.

Scenario Analysis

Best Case Scenario

Companies successfully integrate robust security protocols into AI systems, preventing the misuse of autonomous capabilities while enhancing their positive impact across industries. This leads to trust-building with consumers and regulators alike.

Most Likely Scenario

Organisations incrementally adopt enhanced risk management practices. While some AI systems will face initial regulatory challenges, ongoing improvements will steadily align AI developments with safety standards and ethical practices.

Worst Case Scenario

Failure to address the confused deputy problem could lead to significant AI-related security breaches or malfunctions. This may result in stricter regulations and loss of public trust, hampering AI innovation and deployment.

Strategic Implications

Organisations should invest in dedicated AI risk management teams to navigate evolving security challenges. Collaborating with regulatory bodies could facilitate smoother integration of new security standards, aiding compliance and innovation simultaneously. Furthermore, transparent communication about AI system capabilities and limitations will be vital in maintaining market and consumer trust.

Key Takeaways

  • Understand and address the confused deputy problem as AI systems become more prevalent (Who: AI developers; What: AI security frameworks).
  • Monitor regulatory developments closely to align AI products with upcoming standards (Where: North America, Europe).
  • Invest in AI risk management to future-proof AI technologies against potential security threats.
  • Engage in industry discussions to stay ahead of emerging AI trends.
  • Foster consumer trust through transparent AI capabilities disclosures.

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