CISOs have found themselves more involved with AI teams and often lead their cross-work efforts and AI strategies. But there are not many resources to guide them on what their roles should look like or what they should bring to these meetings.
We have put together a framework for security leaders to further drive AI teams and committees in AI adoption. Meet a clear framework.
If security teams want to play a pivotal role in their organization’s AI journey, they should adopt five clear steps to instantly demonstrate value to AI committees and leadership.
c – Create an AI Asset Inventoryl – Learn what users are doing – Enforce AI policies – Apply AI use cases – Reuse existing frameworks
If you’re looking for a solution that will help you safely take advantage of Genai, check out Harmonic Security.
Now let’s classify a clear framework.
Create an AI Asset Inventory
The basic requirements across regulatory and best practice frameworks, including the EU AI Act, ISO 42001, and NIST AI RMF, maintain AI asset inventory.
Despite its importance, organizations struggle with manual, unsustainable ways to track AI tools.
Security teams can take six key approaches to improving the visibility of their AI assets.
Procurement-Based Tracking – Effective in monitoring new AI acquisitions, but fails to detect AI features added to existing tools. Manual Log Collection – Network traffic and log analysis is lacking in SAAS-based AI, but it can help you identify AI-related activities. Cloud Security and DLP – Solutions like CASB and Netskope offer some visibility, but enforcing policies remains a challenge. ID and OAUTH – Reviewing access logs from providers such as OKTA and ENTRA can help you track your AI application usage. Extend your existing inventory – Classifying AI tools based on risk ensures coordination with enterprise governance, but adoption moves quickly. Professional Tools – Continuous monitoring tools detect AI usage, including personal and free accounts, ensuring comprehensive surveillance. It includes things like harmonic security.
Learning: Moving to Proactive Identification of AI Use Cases
Security teams need to proactively identify the AI applications that employees are using instead of blocking them completely. Users will find workaround otherwise.
By tracking why employees look to AI tools, security leaders can recommend safer and more compliant alternatives to suit their organization’s policies. This insight is invaluable in discussions by AI teams.
Second, if you know how employees use AI, you can provide better training. These training programs will become increasingly important in the deployment of EU AI laws. This requires organizations to offer AI literacy programs.
“AI System Providers and Deployers shall take steps to ensure the most AI literacy of staff and others who deal with the operation and use of AI Systems.”
Implement AI policy
While most organizations implement AI policies, enforcement remains a challenge. Many organizations simply choose to issue AI policies and hope that employees follow the guidance. This approach avoids friction, but offers little enforcement or visibility, and exposes organizations to potential security and compliance risks.
Typically, security teams get one of two approaches.
Secure Browser Control – Some organizations route AI traffic through secure browsers to monitor and manage usage. This approach covers the most generated AI traffic, but has its drawbacks. This often limits the functionality of the Copy Paste, pushing users to an alternate device or browser, and bypassing controls. DLP or CASB solutions – which utilize existing data loss prevention (DLP) or Cloud Access Security Broker (CASB) investments to enforce AI policies. These solutions help track and adjust the use of AI tools, but traditional regular expression-based methods often produce excessive noise. Furthermore, the site classification databases used for blocking are often outdated and lead to inconsistent enforcement.
A proper balance between control and usability is key to successful AI policy enforcement.
Also, if you need to help you build a Genai policy, check out our free Generator Use Policy Generator.
Apply AI use cases to security
While most of this discussion is about ensuring AI, remember that AI teams also want to hear about cool, impactful AI use cases across the business. What is a better way to show that you care about your AI journey than actually implementing it yourself?
Although AI use cases for security are still in the early stages, security teams have already seen several benefits in detection and response, DLP, and email security. Documenting these and bringing these use cases to AI team meetings is powerful. In particular, refer to KPIs on improving productivity and efficiency.
Reuse existing frameworks
Instead of reinventing governance structures, security teams can integrate AI monitoring into existing frameworks such as NIST AI RMF and ISO 42001.
A practical example is NIST CSF 2.0. This includes the “GONTER” feature. This provides a robust foundation for organizational AI risk management strategies Cybersecurity Supply Chain considerations Given the AI-related roles, responsibilities and this extension. AI security governance.
Play a leading role in AI governance for your company
Security teams have a unique opportunity to remember Clear and take a leading role in AI governance.
Creating AI Asset Inventory Apply AI use cases to reuse existing frameworks for implementing user behavior through learning training
By following these steps, CISOS can demonstrate value to AI teams and play an important role in your organization’s AI strategy.
For more information on overcoming Genai’s adoption barriers, see Harmonic Security.
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