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Navigating the Complexities of AI Governance: Introducing TAIMScore™
TAIMScore™ is the Trusted AI Model Score — a 20-control AI governance framework built by HISPI Project Cerebellum. In this episode of The AI Governance Briefing, Dr. Tuboise Floyd, PhD breaks down how TAIMScore™ turns AI accountability into something you can measure, score, and prove.
Four governance domains. Twenty essential controls. Mapped against NIST AI RMF, the EU AI Act, HIPAA, PCI DSS, SOC 2, EU GDPR, and the White House AI Executive Order. If your institution needs a blueprint for AI governance that survives regulatory scrutiny, this is the starting point.
AI is already deployed. The institutions that survive will be the ones that can prove they govern it.
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WHAT YOU WILL LEARN
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∙ Why AI incidents make governance non-negotiable
∙ The Project Cerebellum mission: AI should cause no harm
∙ How the four TAIM domains — GOVERN, MAP, MEASURE, MANAGE — work as an accountability cycle
∙ The 20 TAIMScore™ controls every AI-deploying organization must address
∙ How to crosswalk your AI posture against global regulatory frameworks
∙ Why the AI kill switch is essential governance — not optional
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CHAPTERS
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0:00 Welcome and Introduction
0:28 Real Risks of AI
1:18 Real Generative AI Incidents
2:13 Project Cerebellum: AI Should Cause No Harm
2:48 Vision and Mission
3:33 The Four TAIM Domains
5:01 GOVERN — AI Risk Training (Govern 2.2)
5:31 GOVERN — Supply Chain Policy (Govern 6.1)
6:01 MAP — Establishing Context (Map 1.2)
6:26 MAP — System Requirements (Map 1.6)
6:55 MAP — Third Party Risk (Map 4.1)
7:13 MAP — Impact Documentation (Map 5.1)
7:33 MEASURE — Human Evaluations (Measure 2.2)
7:51 MEASURE — Reliability (Measure 2.5)
8:07 MEASURE — Safety Risk (Measure 2.6)
8:23 MEASURE — Explainability (Measure 2.9)
8:37 MEASURE — Privacy Risk (Measure 2.10)
8:51 MEASURE — Fairness and Bias (Measure 2.11)
9:09 MEASURE — Risk Tracking (Measure 3.1)
9:23 MEASURE — Feedback Loops (Measure 3.3)
9:41 MEASURE — Performance Data (Measure 4.3)
9:57 MANAGE — Resource Allocation (Manage 2.1)
10:19 MANAGE — Unknown Risks (Manage 2.3)
10:35 MANAGE — The Kill Switch (Manage 2.4)
10:55 MANAGE — Post-Deployment Monitoring (Manage 4.1)
11:11 MANAGE — Incident Communications (Manage 4.3)
11:29 TAIMScore™: The Payoff
11:52 Framework Crosswalks — HIPAA, SOC 2, EU AI Act
13:51 Closing and How to Get Involved
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TAIMSCORE™ ASSESSOR WORKSHOP
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Virtual. Instructor-led. One day. Six CPEs. Third Friday of every month.
🔗 humansignal.io/taimscore_assessor_workshop
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FAILURE FILES™ — TAIMScore™ APPLIED
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See TAIMScore™ applied to real institutional failures:
🔗 humansignal.io/failure-files
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RESOURCES
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Project Cerebellum — projectcerebellum.com
HISPI — hispi.org
HISPI LinkedIn Group — linkedin.com/groups/6624427
Email — projectcerebellum@hispi.org
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ABOUT HISPI PROJECT CEREBELLUM
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Project Cerebellum is the AI Governance Think Tank of HISPI — the Holistic Information Security Practitioner Institute. The Trusted AI Model (TAIM) is a flagship framework of 72 controls across four domains that harmonize leading AI governance standards into a practical scoring system. TAIMScore™ was created by Taiye Lambo, Founder and Chief Artificial Intelligence Officer of HISPI.
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ABOUT THE HOST
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Dr. Tuboise Floyd, PhD is the Founder and Chief Sensemaking Officer of Human Signal, Editor in Chief of The AI Governance Record, and a TAIMScore™ Certified Assessor. He holds a PhD from Auburn University and is a member of the HISPI Advocacy & Education Working Group (Project Cerebellum).
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CONNECT
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Website: humansignal.io
Podcast: theaigovernancebriefing.com/podcast
LinkedIn: linkedin.com/in/drtuboisefloyd
Email: tuboise@theaigovernancebriefing.com
Govern the machine. Or be the resource it consumes.
#TAIMScore #AIGovernance #AIAccountability #HISPI #ProjectCerebellum #NISTAIRMF #EUAIAct #AICompliance #FailureFiles #TrustedAIModel #DrTuboiseFloyd #HumanSignal #TheAIGovernanceBriefing #BuilderClass #AIRisk
Companies mentioned in this episode:
- hispi
- Holistic Information Security Practitioner Institute
- Project Cerebellum
- Microsoft
- OpenAI
- ISO
- IEC
- HIPAA
- PCI
- DSS
- SoC2
- EU AI Act
- EU GDPR
- White House AI Executive Order
Takeaways:
- The podcast episode emphasizes the necessity for organizations to establish robust frameworks for AI governance, particularly through the TAIM model.
- The TAIM framework is designed to ensure that AI deployments are safe, secure, responsible, and trustworthy, addressing potential risks proactively.
- A significant focus of the episode is on the real-world examples of AI risks, illustrating the importance of governance in mitigating these risks.
- Effective AI governance requires continuous monitoring and assessment, ensuring that systems remain compliant with evolving regulatory standards.
- The TAIM score provides organizations with a concrete evaluation of their AI governance posture against relevant regulatory frameworks.
- The importance of interdisciplinary collaboration in AI governance is highlighted, underscoring the necessity of diverse perspectives in risk assessment.
This podcast uses the following third-party services for analysis:
OP3 - https://op3.dev/privacy
Transcript
Hey, thanks for being here and welcome.
Speaker A:What you're about to see comes from hispi, the Holistic Information Security Practitioner Institute, and their AI governance think tank known as Project Cerebellum.
Speaker A:Today, we're walking you through the trusted AI model, the Tamescore, a framework built to help organizations bring AI into their world safely, responsibly, and.
Speaker A:And with real confidence.
Speaker A:Let's get into it.
Speaker A:So here's something we can all agree on.
Speaker A:AI is genuinely impressive.
Speaker A:It saves time, cuts down workload, and can do in seconds what used to take people hours.
Speaker A:That's the good news.
Speaker A:But here's the part people don't always want to talk about.
Speaker A:It comes with real risks.
Speaker A:Let me give you a quick example.
Speaker A: Back in June: Speaker A:They had to put out a public statement warning that doing this may actually constitute a breach of confidentiality.
Speaker A:And honestly, that's one of the milder examples.
Speaker A:The risks go much deeper than that.
Speaker A:Which brings us right to the next slide.
Speaker A:Take a look at this list.
Speaker A:These aren't hypothetical scenarios.
Speaker A:These aren't warnings about what might happen someday.
Speaker A:These all actually happened.
Speaker A:Microsoft's AI Chatbot was turned racist by Twitter users in less than one single day.
Speaker A:Lawyers submitted completely fabricated court cases to federal judges, cases made up entirely by AI, and they didn't even realize it.
Speaker A:OpenAI was hit with major data privacy lawsuits.
Speaker A:Deepfakes were weaponized for sextortion targeting real people.
Speaker A:Innocent individuals were wrongfully arrested because of AI misidentification.
Speaker A:And in the financial markets, one AI generated image triggered a full flash crash.
Speaker A:This is the world we're already living in.
Speaker A:So the question isn't, should we govern AI?
Speaker A:The question is, how do we do it well, and that's exactly what TAME is.
Speaker A:Here to answer, Project Cerebellum started with a simple but genuinely bold AI should cause no harm.
Speaker A:That's it.
Speaker A:That's the North Star.
Speaker A:A group of information security practitioners, researchers and governance specialists came together and said, look, AI is coming whether we're ready for it or not.
Speaker A:So instead of reacting to disasters after they happen, let's build the guardrails first.
Speaker A:Let's be proactive about this.
Speaker A:And everything you're about to see flows from that belief.
Speaker A:So here's how they frame the goal.
Speaker A:The vision is to give organizations the guardrails they need to deploy AI that is safe, secure, responsible and trustworthy.
Speaker A:Those four words carry a lot of weight here, and we'll come back to them.
Speaker A:Throughout this presentation, the mission is equally focused to take the best practices and frameworks that already exist across the AI world and actually harmonize them.
Speaker A:Make them practical, make them accessible, make them work for real organizations in the real world.
Speaker A:Because, let's be honest, nobody has time to wade through a dozen different regulatory frameworks and figure out how they all fit together.
Speaker A:That's the work HISP has already done for you.
Speaker A:Alright, here's the big picture.
Speaker A:TAME is built around four core domains.
Speaker A:Think of them as the four pillars of responsible AI governance.
Speaker A:First, govern.
Speaker A:This is about leadership, culture and accountability.
Speaker A:Who's responsible?
Speaker A:What are the rules?
Speaker A:Then map.
Speaker A:Before you deploy anything, you need to actually understand your landscape.
Speaker A:Who are the stakeholders?
Speaker A:Where are the risks hiding?
Speaker A:Next, measure.
Speaker A:Because what gets measured gets managed.
Speaker A:This is where you test and evaluate your AI systems against real quantifiable standards.
Speaker A:And finally manage.
Speaker A:This is where everything becomes action.
Speaker A:Monitoring live systems, responding to incidents and when necessary, knowing exactly when to pull the plug.
Speaker A:And here's what's important.
Speaker A:These four don't work in a straight line.
Speaker A:They form a continuous cycle.
Speaker A:You don't check this box once and move on.
Speaker A:This is ongoing governance.
Speaker A:This visual gives you the full picture of how the TAME framework fits together.
Speaker A:What I want you to notice is that it's not a one way street.
Speaker A:Each domain feeds into the next and it circles back.
Speaker A:GOVERN informs.
Speaker A:How you map, mapping shapes, what you measure, measuring drives, how you manage and what you learn from managing feeds right back into governance.
Speaker A:It's a living, breathing system, not a checkbox exercise.
Speaker A:Let's start with govern and the control, I think is the most foundational in the entire framework.
Speaker A:Govern 2.2 says that your people and your partners need to actually understand AI risk.
Speaker A:Not a quick email blast, not a slide in the onboarding deck.
Speaker A:Real structured AI risk management training.
Speaker A:Because here's the thing, you can have the best policies in the world, but if your employees and vendors don't understand why those policies exist, they'll work around them without even realizing it.
Speaker A: : Speaker A:Then there's govern 6.1 and I think this one gets overlooked far too often.
Speaker A:This control is about your supply chain.
Speaker A:Most AI deployments today rely heavily on third party models and external data sets and vendor tools you don't fully control.
Speaker A:So if your policies don't specifically address the risks that come with those third parties, you've got a blind spot.
Speaker A:A significant one.
Speaker A:Now we move into map and this is where you do your homework before anything gets deployed.
Speaker A:Map 1.2 is about knowing who's in the room.
Speaker A:Who are the stakeholders involved in this AI system and are they diverse enough?
Speaker A:You want interdisciplinary perspectives here, not just your tech team.
Speaker A:Legal, HR compliance, end users.
Speaker A:They all see different risks.
Speaker A:Map 1.6 is one of my personal favorites because it's so practical.
Speaker A:It says write down what your system is supposed to do.
Speaker A:Things like this system shall respect user privacy.
Speaker A:Sounds obvious, right?
Speaker A:But you'd be genuinely surprised how many AI deployments skip this step entirely.
Speaker A:And it goes further.
Speaker A:It says you have to think about the socio technical implications.
Speaker A:Not just does the tech work, but what does this mean for the people it actually touches.
Speaker A:Map 4.1 brings in the legal and third party angle again.
Speaker A:You need to formally document your approach to identifying the legal and operational risks tied to AI components and data sources you don't fully own or control.
Speaker A:If you're using it, you're accountable for it.
Speaker A:And map 5.1 asks a really important question for every identified impact.
Speaker A:What's the likelihood it actually happens and how bad could it be?
Speaker A:You have to document both sides, the potential benefits and the potential harms.
Speaker A:Because looking at only the upside isn't governance.
Speaker A:That's just optimism.
Speaker A:Now we're into measure.
Speaker A:And this is where a lot of organizations fall short because it requires actual rigor.
Speaker A:Measure 2.2 if your AI system is being evaluated using real human subjects, you need proper protections in place.
Speaker A:And the people you test on need to actually represent the people who use the system in the real world.
Speaker A:No cherry picking your test group.
Speaker A:Measure 2.5 can you prove your system is reliable?
Speaker A:Not we think it works.
Speaker A:Not it seemed fine in testing documented, validated evidence that it performs as intended and critically.
Speaker A:You also have to document what it doesn't do well.
Speaker A:Measure 2.6 is about safety.
Speaker A:You need regular safety risk evaluations running.
Speaker A:And your system needs to be designed to fail safely.
Speaker A:Meaning when something goes wrong and at some point something will, it doesn't make the situation catastrophically worse.
Speaker A:Measure 2.9 covers explainability.
Speaker A:Can someone look at your AI's output and actually understand it?
Speaker A:Can you explain why the system made the decision it made?
Speaker A:This matters enormously for building trust and for surviving an audit.
Speaker A:Measure 2.10 zeroes in on privacy.
Speaker A:Specifically, what data is the system touching?
Speaker A:What are the risks?
Speaker A:This needs to be formally documented, not just assumed to be fine because nobody has complained yet.
Speaker A:Measure 2.11 is where we tackle fairness and bias head on.
Speaker A:Any biases you Flagged back in the map phase.
Speaker A:Here's where you actually test for them and document every result.
Speaker A:This isn't optional.
Speaker A:This control maps to standards, including the Illinois bipa.
Speaker A:So there are real legal stakes on the line.
Speaker A:Measure 3.1.
Speaker A:You need ongoing mechanisms to catch risks that that weren't there on day one.
Speaker A:AI systems drift over time.
Speaker A:Data changes, new risks emerge.
Speaker A:You have to be tracking this continuously, not just at launch.
Speaker A:Measure 3.3 is about giving users a real voice.
Speaker A:If someone interacts with your AI and something goes wrong or they just disagree with the outcome, is there a clear, accessible way to report it or appeal it?
Speaker A:If not, you're essentially flying blind on real world performance.
Speaker A:And measure 4.3 brings it home.
Speaker A:Can you show with actual data that your governance efforts are working?
Speaker A:Are things genuinely improving over time?
Speaker A:This is what separates a mature governance program from from checkbox compliance and finally, Manage.
Speaker A:This is where governance becomes action.
Speaker A:Manage 2.1 asks a question that's easy to skip right past.
Speaker A:Do you actually have the resources to manage the risks you've identified?
Speaker A:And if not, should you maybe be looking at a non AI solution instead?
Speaker A:Sometimes the most responsible choice is to not deploy AI, and that's okay.
Speaker A:Manage 2.3 is about being ready for surprises.
Speaker A:Because with AI there will always be surprises.
Speaker A:You need documented playbooks ready to go for responding to risks you didn't see coming, not figuring it out as you go planning ahead.
Speaker A:Manage 2.4 and this one is non negotiable.
Speaker A:You must have the ability to turn the system off, to override it, to suspend or fully deactivate it.
Speaker A:If it starts behaving in ways you didn't intend, the kill switch matters.
Speaker A:This isn't pessimism, it's just good engineering and good governance.
Speaker A:Manage 4.1 is about what happens after go live.
Speaker A:Because that's when the real work begins.
Speaker A:Post deployment monitoring isn't optional.
Speaker A:You need active plans for incident response.
Speaker A:And you need to know ahead of time exactly how you decommissioned the system if you had to.
Speaker A:And manage 4.3 closes the loop.
Speaker A:When incidents happen, and they will, the right people need to know.
Speaker A:Communication plans, recovery documentation, lessons learned.
Speaker A:This is how organizations don't just survive AI incidents.
Speaker A:They get better because of them.
Speaker A:Now here's where it all comes together.
Speaker A:The TAME score.
Speaker A:This is the payoff for everything we've just walked through.
Speaker A:A visual scoring system that maps your organization's entire AI governance posture against the major regulatory frontline frameworks that matter most right now.
Speaker A:The Tames score evaluates your compliance across HIPAA, PCI, DSS, SoC2, the EU AI Act, EU GDPR, and the White House AI Executive Order.
Speaker A:Each of the 20 controls we just covered gets evaluated across three dimensions people, process and data and technology.
Speaker A:And your AI systems are assessed against seven trustworthy transparency, accountability, impartiality, inclusion, security and privacy, reliability and safety and robustness.
Speaker A:No more vague conversations about whether you think you're compliant.
Speaker A:The TAME score gives you something concrete to point to and something concrete to improve.
Speaker A:And that's a wrap on the trusted AI model.
Speaker A:Here's the thing.
Speaker A:AI governance doesn't have to be overwhelming.
Speaker A:Frameworks like TAME exist precisely to make this manageable.
Speaker A:You don't have to figure all of this out on your own.
Speaker A:If anything you saw today sparked a question, or if you want to get more involved in the conversation, here's how to find the join the LinkedIn group, visit projectcerebellum.com or reach them directly by email at projectcerebellumispi.org and for the full scope of Hispii's work, head to hispii.org the goal is simple AI that works for people, not against them.
Speaker A:Thanks so much for watching, and let's build something trustworthy together.
