Quick Answer: Creating an AI policy is an important first step, but it is not the same as AI governance. Organizations deploying AI need to understand how different AI systems work, where risks exist, who owns accountability, and how AI use is monitored and controlled.
In this episode of Trust Issues, Dallas Bishoff, President of Process 360 and member of multiple ISO standards committees, explains why AI governance requires structure, oversight, risk assessment, and continuous improvement, not just a policy document stored in a shared folder.
Key Takeaways
- An AI policy alone does not constitute AI governance.
- Many organizations are using AI without fully understanding its risks and failure modes.
- Different categories of AI require different governance approaches.
- Agentic AI introduces unique identity, access, and permission challenges.
- Shadow AI is already present inside many organizations.
- Effective governance requires policies, procedures, monitoring, and accountability.
- The same AI tool can present very different risks depending on how it is used.
- ISO 42001 provides a framework for managing AI responsibly and defensibly.
Table of Contents
- Why an AI Policy Isn't Enough
- The AI Knowledge Gap
- Understanding Different Types of AI
- The Identity and Permission Risks of Agentic AI
- The Growing Challenge of Shadow AI
- A Policy Is Not a Governance System
- Why AI Risk Depends on the Use Case
- AI Governance Will Be Messy and That's Normal
- What Organizations Should Do Next
- Listen to the Full Episode
- Frequently Asked Questions
Watch the full episode on other platforms:
Apple Podcasts: https://go.fame.so/ep23apple
Spotify: https://go.fame.so/ep23spotify
Why an AI Policy Isn't Enough
Many organizations believe they have addressed AI risk once they draft a policy and distribute it internally.
According to Dallas Bishoff, that assumption creates a dangerous false sense of security.
A policy document can communicate expectations and provide high-level guidance, but it does not create accountability, oversight, monitoring, or governance. When regulators, customers, auditors, or business leaders begin asking questions about how AI is managed, a policy alone will not provide sufficient answers.
AI governance requires organizations to demonstrate how risks are evaluated, how controls are implemented, and how decisions are made throughout the AI lifecycle.
In other words, governance is a system, not a document.
The AI Knowledge Gap
One of the biggest challenges facing organizations today is a lack of AI literacy.
Many business leaders and employees understand how to use AI tools to generate content, summarize information, or automate tasks. Far fewer understand how those systems can fail.
That distinction matters.
Organizations often focus on AI's capabilities while spending little time evaluating its limitations, vulnerabilities, or potential business impacts.
Dallas emphasizes that many risks emerge from this gap in understanding. If organizations do not understand how AI systems can produce inaccurate outputs, introduce bias, make faulty decisions, or expose sensitive information, they are unlikely to implement effective governance controls.
Before organizations can govern AI, they must first understand it.
Understanding Different Types of AI
A common mistake in AI governance is treating all AI technologies as if they are the same.
They are not.
As discussed during the episode, organizations are increasingly deploying different categories of AI, including:
- Predictive AI, which forecasts outcomes based on historical data.
- Generative AI, which creates content such as text, images, audio, or code.
- Agent AI, which assists with tasks and workflows.
- Agentic AI, which can initiate actions and make decisions with varying levels of autonomy.
Each category presents different risks, operational considerations, and governance requirements.
Attempting to manage all AI systems under a single, generic policy may leave significant gaps in oversight and risk management.
Effective governance begins by understanding what types of AI are being used and how those systems affect business operations.
The Identity and Permission Risks of Agentic AI
Agentic AI introduces unique security considerations because it often operates using the permissions and access rights of the individual who initiates the process.
This creates two common problems.
First, if an employee has excessive permissions, the AI agent may inherit those same permissions and gain access to information it does not actually need.
Second, if the employee lacks required permissions, the AI system may fail to perform its intended function, creating confusion and operational issues.
According to Dallas, identity management and permissions remain some of the most common challenges organizations encounter when deploying AI solutions.
Organizations should evaluate identity governance, access control models, and privilege management before introducing autonomous AI capabilities into business processes.
The Growing Challenge of Shadow AI
Many organizations believe they know how employees are using AI.
In practice, the reality can be very different.
Dallas describes situations where network assessments reveal substantially more AI-related activity than leadership expected. Employees often adopt public AI tools without formal approval, particularly when productivity pressures encourage experimentation.
Even organizations that provide approved AI platforms sometimes discover employees using alternative services outside sanctioned environments.
This behavior creates risks related to data exposure, intellectual property protection, compliance requirements, and governance oversight.
Rather than focusing exclusively on restricting AI usage, organizations should work to understand current AI adoption, establish clear expectations, and build visibility into how these tools are being used.
You cannot govern what you cannot see.
A Policy Is Not a Governance System
One of the most important distinctions discussed during the episode is the difference between documentation and governance.
A single document that tries to serve as a policy, standard, procedure, guidance document, and compliance framework simultaneously becomes difficult to implement and even harder to maintain.
A more effective governance model includes:
- An overarching AI policy.
- Supporting standards and procedures.
- Risk management processes.
- Defined roles and responsibilities.
- Monitoring and oversight mechanisms.
- Integration with existing security and privacy programs.
This layered approach creates a sustainable governance structure capable of adapting as AI technologies evolve.
Why AI Risk Depends on the Use Case
AI risk is not determined solely by the technology itself.
It is determined by how the technology is used.
A generative AI solution helping a research team analyze market trends may present relatively low organizational risk. The same platform supporting hiring, lending decisions, healthcare recommendations, or regulatory compliance activities may require significantly greater governance and oversight.
This means organizations cannot evaluate AI tools in isolation.
They must evaluate individual use cases.
Risk assessments should consider business impact, potential harm, regulatory exposure, data sensitivity, decision-making authority, and the consequences of inaccurate outputs.
The same tool can have dramatically different risk profiles depending on the context.
AI Governance Will Be Messy and That's Normal
AI governance is still evolving.
Organizations will encounter unexpected behaviors, inaccurate outputs, failed controls, and new risks as technologies mature. Governance programs will continue adapting alongside rapid changes in AI capabilities.
That reality should not discourage organizations from adopting AI.
Instead, it reinforces the importance of approaching AI with appropriate oversight, continuous learning, and realistic expectations.
No governance program will eliminate all risk.
The objective is not perfection.
The objective is understanding risk, implementing reasonable safeguards, and creating accountability for how AI is used across the organization.
Organizations that balance innovation with governance will be better positioned to capture AI's benefits while managing its risks.
What Organizations Should Do Next
For organizations just beginning their AI governance journey, the first priority is visibility.
Before creating policies, organizations should understand:
- What AI tools are currently being used.
- Which departments are using them.
- What data is being processed.
- What business decisions AI is influencing.
- What risks exist for each use case.
From there, organizations can strengthen AI literacy, establish governance responsibilities, define risk management processes, and align their approach with frameworks such as ISO 42001.
Effective governance starts with understanding what is already happening.
Ready to Build an AI Governance Program?
AI adoption is accelerating across every industry, but successful organizations recognize that governance must evolve alongside innovation.
👉 Book a meeting with BEMO's cybersecurity, compliance, and AI governance experts to assess your current AI landscape and develop a practical governance strategy.
Want more conversations with cybersecurity, compliance, and AI leaders? Subscribe to the Trust Issues podcast for insights on emerging technologies, AI risk management, cybersecurity, compliance, and governance.
Listen to the Full Episode
🎙️ Apple Podcasts: Listen on Apple Podcasts
🎙️ Spotify: Listen on Spotify
🎙️ YouTube: Watch on YouTube
Frequently Asked Questions
What is ISO 42001 and why does it matter?
ISO 42001 is the first international standard for AI management systems. It provides a framework for managing AI risks, governance, accountability, and continuous improvement.
Isn't an AI policy enough?
No. A policy establishes expectations, but effective AI governance also requires procedures, oversight, monitoring, accountability, and risk management processes.
What's the difference between predictive, generative, and agentic AI?
Predictive AI forecasts outcomes, generative AI creates content, and agentic AI can take actions on behalf of users. Each introduces different operational, security, and governance considerations.
Can the same AI tool have different risk levels?
Yes. Risk is determined by the use case. An AI tool used for research may be low risk, while the same tool used for employment or compliance decisions could require significantly more oversight.
What should organizations do first if they have no AI governance program?
Start by understanding what AI tools are already being used across the organization, what data they access, and what business processes they influence. Visibility is the foundation of effective AI governance.
We've got more resources for you to become a cyber-pro.
Leave us a comment!