Skip to main content

Generative AI Governance: Using the NIST Framework to Build Trust, Reduce Risk, and Lead Secure AI Adoption


Generative AI has moved faster than nearly any technology security leaders have dealt with. Tools that can generate text, code, images, and data insights are now embedded into productivity platforms, security tooling, development workflows, and business operations—often before security teams are formally involved.


For CISOs, this creates a familiar but amplified challenge: innovation is happening faster than governance, and unmanaged generative AI introduces material risk across confidentiality, integrity, availability, compliance, and trust.


For aspiring information security professionals, AI governance represents a growing and valuable discipline where strategic thinking matters just as much as technical depth.


The good news? We don’t need to invent governance from scratch. NIST’s AI Risk Management Framework (AI RMF) provides a practical, flexible structure that security leaders can use today to govern generative AI responsibly and defensibly.

Why Generative AI Governance Matters to Security Leadership

Generative AI risk is not hypothetical. Organizations are already encountering:

  • Data leakage through AI prompts and training data
  • Hallucinated or incorrect outputs used in decision-making
  • Intellectual property exposure
  • Model bias and fairness concerns
  • Unauthorized use of public AI tools by employees
  • Regulatory and legal uncertainty

Unlike traditional systems, generative AI is probabilistic, opaque, and often externally hosted, which complicates traditional security controls.

Governance becomes the foundation that answers three critical CISO questions:

  1. Where is generative AI being used?
  2. What risks does it introduce to the organization?
  3. Who is accountable when things go wrong?

Overview of the NIST AI Risk Management Framework (AI RMF)

The NIST AI RMF is built around four core functions:

  1. Govern
  2. Map
  3. Measure
  4. Manage

These functions are continuous, iterative, and intentionally non-prescriptive—making them well-suited for fast-evolving technologies like generative AI.

Let’s break them down through a governance and security lens.

1. GOVERN: Establishing Accountability and Guardrails

The Govern function is the most important—and often the most overlooked. Without governance, technical controls will always lag behind usage.

For generative AI, governance should answer:

  • Who approves AI use cases?
  • Who owns AI risk?
  • What policies apply to AI-generated outputs?
  • How do ethics, legal, security, and privacy intersect?

Key Governance Actions for CISOs

  • Establish an AI governance committee that includes:
    • Security
    • Legal/compliance
    • Privacy
    • Engineering
    • Business stakeholders
  • Define acceptable and prohibited AI use cases
  • Create policies for:
    • Data usage in prompts
    • Third-party AI tools
    • Model training and fine-tuning
    • Human oversight and validation
  • Align AI governance with existing frameworks (ISO 27001, NIST CSF, SOC 2)

For Aspiring Professionals

Understanding AI governance shows that security is not just about tools—it’s about decision-making, accountability, and risk ownership. This is an area where junior professionals can add real strategic value.

2. MAP: Understanding Context, Data, and Impact

The Map function focuses on understanding how AI systems are used and what risks they introduce in context.

For generative AI, mapping means documenting:

  • Where AI is embedded (SaaS tools, internal applications, copilots)
  • What data flows into and out of models
  • Who consumes the outputs
  • What decisions are influenced by AI-generated content

Practical Mapping Questions

  • Are employees using public AI tools with corporate data?
  • Are AI-generated outputs customer-facing?
  • Is AI influencing security decisions (alerts, triage, remediation)?
  • What data classifications are involved?

This step often uncovers shadow AI usage, similar to shadow IT—but more dangerous because data exposure is harder to detect.

3. MEASURE: Assessing and Quantifying AI Risk

Traditional risk assessments don’t fully translate to generative AI. The Measure function encourages organizations to evaluate risk using qualitative and quantitative signals.

Key risk dimensions include:

  • Accuracy and reliability (hallucinations)
  • Bias and fairness
  • Security vulnerabilities
  • Privacy exposure
  • Explainability and transparency
  • Operational resilience

Measurement in Practice

  • Model output testing and validation
  • Red teaming AI systems
  • Prompt injection testing
  • Privacy impact assessments (PIAs)
  • Vendor risk assessments for AI providers

For CISOs, this is where AI risk becomes defensible at the board level—measured, documented, and tracked.

4. MANAGE: Mitigating Risk and Responding to Change

The Manage function is about operationalizing controls and continuously improving them as AI systems evolve.

Key controls include:

  • Technical safeguards (DLP, API monitoring, access controls)
  • Human-in-the-loop requirements for high-risk use cases
  • Logging and auditability for AI interactions
  • Incident response playbooks specific to AI failures
  • Continuous monitoring for model drift and misuse

Governance here is not static. As models update and new use cases emerge, controls must adapt.


Why This Matters for Careers in Information Security

For aspiring InfoSec professionals, generative AI governance is a career accelerant:

  • It blends security, risk, policy, and business alignment
  • It demonstrates strategic thinking beyond tools
  • It’s highly visible to leadership and boards
  • It’s an area with limited experienced practitioners—yet

Understanding and speaking to the NIST AI RMF immediately differentiates candidates who “understand security” from those who understand security leadership.

Final Thoughts: Governance Is Security Leadership

Generative AI is not just another technology—it changes how data is processed, how decisions are made, and how trust is established.

For CISOs, governance is how you stay ahead of both risk and regulators. For security professionals, it’s how you move from operator to leader.

Using the NIST AI Risk Management Framework provides a shared language, a defensible structure, and a practical starting point for responsible generative AI adoption.

Govern it early, govern it well, and generative AI becomes an enabler—not a liability.



Labels:

Comments

Post a Comment

Popular posts from this blog

Asset Management - Physical Devices - What do you have? Do you know?

Asset management and inventorying your physical systems, we all know we should do it, and I am sure most try.  I am not going to talk about the should have, would have or could have. Instead, I am going to focus on the risks associated with the NIST CSF control ID-AM.1.   The control simply states, “Physical devices and systems within the organization are inventoried.”  At the simplest level, this control is saying that the organization inventories all physical systems that are apart of the information system. In my opinion, the control is foundational because how can you secure something if you don't know it exists.  If you are not inventorying your systems, how do you know if they have adequate controls to protect the data and network.   If you had a breach of data, would you know what type of data was involved, or would you even know if you had a breach?  To further extend this, how can you perform a risk assessment on the system to understand and relay ...
Post a Comment
Read more

Vulnerability Management… It’s easy - Planning

I am sure you have had either consultants, vendors, or heard at a conference that vulnerability management is foundational security control.  While I agree that it is an essential control, I also understand that it is challenging to implement.  Vulnerability management is not just to pick a tool, scan, and fix issues.  Many components make it a complicated journey.  This series will attempt to help break it down and give you ideas on how this complex service and be delivered effectively.    Planning   Objective When you start, I recommend creating a targeted objective and set of measures against your objective.   Ensure that you keep in mind your organization’s culture, politics, and risk appetite as you are developing your objective.   I have seen some target just “critical” systems for regulatory compliance, whereas others have targeted their entire enterprise.   No matter your scope, keep in mind your team’s current resource...
Post a Comment
Read more

The Detect Function in NIST CSF 2.0: The Risk of Seeing Too Late—or Too Much

In NIST Cybersecurity Framework 2.0 (CSF 2.0) , the Detect function represents the organization’s ability to identify the occurrence of a cybersecurity event in a timely and reliable manner . While Protect focuses on reducing the likelihood of compromise, Detect determines how quickly and how accurately an organization recognizes that something has gone wrong. For CISOs and security leaders, detection is where many programs quietly fail. Not due to a lack of tools, but due to poor signal quality, unclear objectives, and misalignment with business impact. Detection that is late, noisy, or misunderstood can be as damaging as no detection at all. Official NIST CSF 2.0 guidance is available here: https://www.nist.gov/publications/nist-cybersecurity-framework-csf-20 What the Detect Function Is (and What It Enables) Under CSF 2.0, the Detect (DE) function focuses on outcomes related to: Continuous monitoring Anomalies and event detection Security logging and analysis Threat intelligence ...
Post a Comment
Read more