AI is genuinely impressive. It saves time, cuts down workload, and can do in seconds what used to take people hours. That is the good news. Here is the part people do not always want to talk about: it comes with real risks.
In June 2023, the Australian Research Council found that applicants had submitted grant proposals written by ChatGPT. The Council had to issue a public statement warning that doing this may actually constitute a breach of confidentiality. That is one of the milder examples. The risks go much deeper.
Microsoft's AI chatbot was turned racist by Twitter users in less than a single day. Lawyers submitted completely fabricated court cases to federal judges — cases made up entirely by AI — and did not realize it. OpenAI was hit with major data privacy lawsuits. Deepfakes were weaponized for sextortion targeting real people. Innocent individuals were wrongfully arrested because of AI misidentification. In the financial markets, one AI-generated image triggered a full flash crash.
None of those are hypothetical. None of those are warnings about what might happen someday. They already happened. This is the world we are already living in.
So the question is not should we govern AI. The question is how do we do it well. That is exactly what the Trusted AI Model — and its scoring instrument, the TAIMScore™ — is here to answer.
The Real Risks Are Not Hypothetical
Every institution deploying AI is inheriting exposure the technology did not have five years ago. The failure record is not speculative — it is documented. A chatbot invented a bereavement refund policy that did not exist, and a Canadian tribunal held the airline liable for the hallucination. An insurer deployed an algorithm that denied post-acute care claims at scale, and federal lawsuits followed. A real-estate platform repurposed a consumer estimation tool as an asset-pricing engine, and the company lost $881 million before shutting the program down.
Every one of those cases is scored against TAIMScore™ controls in the Failure Files™ series. The pattern is consistent: the system did what systems do. The controls that should have bounded its authority did not exist, did not fire, or were never tested.
Regulators have moved. The EU AI Act is law. The White House AI Executive Order is in force. State-level AI accountability legislation is compounding. Institutions without a framework will be audited anyway — they will just be audited without a score to defend.
Project Cerebellum: AI Should Cause No Harm
Project Cerebellum is the AI Governance Think Tank of HISPI — the Holistic Information Security Practitioner Institute. It started with a simple but genuinely bold premise: AI should cause no harm. That is the North Star. A group of information security practitioners, researchers, and governance specialists came together and said: AI is coming whether we are ready for it or not. So instead of reacting to disasters after they happen, let us build the guardrails first. Let us be proactive about this.
Everything that follows in the TAIMScore™ framework flows from that belief.
The Vision and the Mission
The vision is to give organizations the guardrails they need to deploy AI that is safe, secure, responsible, and trustworthy. Those four words carry weight throughout the framework. Every control maps back to one of them.
The mission is to take the best practices and frameworks that already exist across the AI world and actually harmonize them — make them practical, make them accessible, make them work for real organizations in the real world. Nobody has time to wade through a dozen different regulatory frameworks and figure out how they all fit together. That is the work HISPI has already done.
"The mission is to take the best practices that already exist and harmonize them — make them practical, make them accessible, make them work for real organizations in the real world."
The Four TAIM Domains
The TAIM framework is built around four core domains. Think of them as the four pillars of responsible AI governance.
GOVERN is about leadership, culture, and accountability. Who is responsible? What are the rules?
MAP is about understanding the landscape before deployment. Who are the stakeholders? Where are the risks hiding?
MEASURE is evaluation. Because what gets measured gets managed. This is where AI systems are tested against real, quantifiable standards.
MANAGE is where everything becomes action. Monitoring live systems, responding to incidents, and — when necessary — knowing exactly when to pull the plug.
The four do not work in a straight line. They form a continuous cycle. This is not a box to check once and move on. GOVERN informs how you MAP. Mapping shapes what you MEASURE. Measuring drives how you MANAGE. What you learn from managing feeds right back into governance. It is a living, breathing system, not a checkbox exercise.
GOVERN — Leadership, Culture, and Accountability
GOVERN is the most foundational domain in the entire framework. An institution without a functioning GOVERN layer does not have AI governance — it has AI deployment with paperwork.
2.2
AI Risk Management Training
Your people and your partners need to actually understand AI risk. Not a quick email blast. Not a slide in the onboarding deck. Real, structured AI risk management training. You can have the best policies in the world, but if your employees and vendors do not understand why those policies exist, they will work around them without even realizing it. This control maps directly to ISO/IEC 42001 Section 7.2 on competence.
6.1
Supply Chain Policy
This one gets overlooked far too often. Most AI deployments today rely heavily on third-party models, external data sets, and vendor tools you do not fully control. If your policies do not specifically address the risks that come with those third parties, you have a blind spot — a significant one.
MAP — Know Your Landscape Before You Deploy
MAP is where you do your homework before anything gets deployed. This is context — who is in the room, what the system is supposed to do, what can go wrong, and how bad it could be.
1.2
Establishing Context — Who Is in the Room
Who are the stakeholders involved in this AI system, and are they diverse enough? You want interdisciplinary perspectives here — not just your tech team. Legal, HR, compliance, and end users all see different risks.
1.6
System Requirements — Write Down What It Is Supposed to Do
Write down what your system is supposed to do. Statements like this system shall respect user privacy. Sounds obvious — but you would be genuinely surprised how many AI deployments skip this step entirely. And it goes further: you have to think about the socio-technical implications. Not just does the tech work, but what does this mean for the people it actually touches.
4.1
Third-Party Risk
Formally document your approach to identifying the legal and operational risks tied to AI components and data sources you do not fully own or control. If you are using it, you are accountable for it.
5.1
Impact Documentation
For every identified impact: what is the likelihood it actually happens, and how bad could it be? Document both sides — the potential benefits and the potential harms. Looking at only the upside is not governance. That is optimism.
MEASURE — What Gets Measured Gets Managed
MEASURE is where a lot of organizations fall short — because it requires actual rigor. This is where governance stops being policy and becomes evidence. Nine controls live here. A system that cannot be measured cannot be scored. A system that cannot be scored cannot be defended.
2.2
Human Evaluations
If your AI system is being evaluated using real human subjects, you need proper protections in place. The people you test on need to actually represent the people who use the system in the real world. No cherry-picking your test group.
2.5
Reliability
Can you prove your system is reliable? Not we think it works. Not it seemed fine in testing. Documented, validated evidence that it performs as intended — and, critically, you also have to document what it does not do well.
2.6
Safety Risk
Regular safety risk evaluations must be running. Your system needs to be designed to fail safely — meaning when something goes wrong, and at some point something will, it does not make the situation catastrophically worse.
2.9
Explainability
Can someone look at your AI's output and actually understand it? Can you explain why the system made the decision it made? This matters enormously for building trust and for surviving an audit.
2.10
Privacy Risk
What data is the system touching? What are the risks? This needs to be formally documented — not just assumed to be fine because nobody has complained yet.
2.11
Fairness and Bias
Any biases you flagged back in the MAP phase — here is where you actually test for them and document every result. This is not optional. This control maps to standards including the Illinois BIPA, so there are real legal stakes on the line.
3.1
Risk Tracking
Ongoing mechanisms to catch risks that were not there on day one. AI systems drift over time. Data changes. New risks emerge. Track this continuously — not just at launch.
3.3
Feedback Loops
Give users a real voice. 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 or appeal it? If not, you are flying blind on real-world performance.
4.3
Performance Data
Can you show — with actual data — that your governance efforts are working? Are things genuinely improving over time? This is what separates a mature governance program from checkbox compliance.
MANAGE — Where Governance Becomes Action
MANAGE is where governance becomes action. This is the operational domain: resources, playbooks, the kill switch, monitoring, and incident communication.
2.1
Resource Allocation
Do you actually have the resources to manage the risks you have identified? If not, you should maybe be looking at a non-AI solution instead. Sometimes the most responsible choice is to not deploy AI — and that is okay.
2.3
Unknown Risks — Be Ready for Surprises
With AI there will always be surprises. You need documented playbooks ready to go for responding to risks you did not see coming — not figuring it out as you go. Plan ahead.
2.4
The Kill Switch — Non-Negotiable
You must have the ability to turn the system off — to override it, to suspend it, to fully deactivate it if it starts behaving in ways you did not intend. The kill switch matters. This is not pessimism. It is good engineering and good governance.
4.1
Post-Deployment Monitoring
What happens after go-live is when the real work begins. Post-deployment monitoring is not optional. You need active plans for incident response — and you need to know ahead of time exactly how you would decommission the system if you had to.
4.3
Incident Communications
When incidents happen — and they will — the right people need to know. Communication plans, recovery documentation, lessons learned. This is how organizations do not just survive AI incidents. They get better because of them.
The TAIMScore™: The Payoff
This is where it all comes together. The TAIMScore™ is the payoff for everything the framework just mapped out — a visual scoring system that measures an organization's entire AI governance posture against the major regulatory frameworks that matter most right now.
The TAIMScore™ evaluates compliance across HIPAA, PCI DSS, SOC 2, the EU AI Act, EU GDPR, and the White House AI Executive Order. Each of the twenty controls is evaluated across three dimensions — people, process, and data and technology.
Scored Against Seven Trustworthy Properties
No more vague conversations about whether you think you are compliant. The TAIMScore™ gives you something concrete to point to — and something concrete to improve.
For federal contractors, the score is procurement language. For healthcare systems, it is regulatory posture. For financial institutions, it is fiduciary evidence. For universities and state agencies, it is the difference between a governance story an auditor accepts and one they do not.
The TAIMScore™ Assessor Workshop — virtual, instructor-led, one day, six CPEs, third Friday of every month — is the pathway for practitioners who need to run the assessment inside their own institutions. The workshop is not a certification of the institution. It is a credential for the practitioner authorized to deliver the score.
Episode Chapters
The AI Governance Briefing · 16:51
Apply the Framework
AI governance does not have to be overwhelming. Frameworks like TAIM exist precisely to make this manageable. You do not have to figure all of this out on your own.
TAIMScore™ is the mechanism. The Failure Files™ are the evidence base. The Trust Gap, GASP™, and L.E.A.C. Protocol™ are the supporting instruments. Together they form the Human Signal canon — a governance architecture built for institutional operators who own the outcomes.
Every Failure File™ in the Human Signal library is a TAIMScore™ score of an institutional failure. Air Canada scored against GOVERN and MANAGE. UnitedHealthcare scored against GOVERN and MEASURE. Zillow scored against MAP and MEASURE. The score is never rhetorical. It is structural. That is the point.
The goal is simple: AI that works for people, not against them.
Govern the machine. Or be the resource it consumes.
Apply the Framework
TAIMScore™ Assessor Workshop — Virtual. Instructor-led. One day. Six CPEs. Third Friday of every month. The credential for practitioners authorized to deliver the score.
→ Register for the Workshop → TAIMScore™ OverviewFailure Files™ — Every case scored against TAIMScore™. See the framework applied to real institutional AI governance failures.
→ All Failure Files™ → The Trust Gap → GASP™ DiagnosticProject Cerebellum & HISPI — Join the AI Governance Think Tank.
→ HISPI on LinkedIn → projectcerebellum.com → hispi.org → ✦ Underwrite Human SignalRelated in the Failure Files™