Understanding Both Approaches
What Holistic AI Does Well
Holistic AI has built a comprehensive platform for AI risk management and bias auditing. Their strengths include:
- Bias and Fairness Testing: Rigorous statistical analysis of model outputs across protected characteristics. Their testing methodologies can identify disparate impact before deployment.
- Compliance Documentation: Structured reports mapping to regulatory frameworks like NYC Local Law 144, EU AI Act risk assessments, and EEOC guidelines.
- Risk Assessment Frameworks: Systematic approaches to identifying and categorizing AI risks during the development and deployment lifecycle.
- Expert Consulting: Access to AI ethics and compliance expertise for complex regulatory questions.
Holistic AI excels at answering the question: "Was our AI system fair and compliant when we assessed it?"
What GLACIS Does Differently
GLACIS approaches AI governance from a fundamentally different angle: continuous, cryptographic proof generation at runtime.
- Runtime Attestation: Every AI inference generates tamper-evident proof that specific controls executed correctly. Not sampling. Not periodic checks. Every decision.
- Cryptographic Evidence: Proof is mathematically verifiable and tamper-evident. You can’t retroactively fabricate compliance evidence.
- Control Execution Proof: Demonstrates that guardrails, filters, content policies, and safety mechanisms were active and enforced at the moment of decision.
- Audit Trail Infrastructure: Creates an immutable record that satisfies logging requirements in EU AI Act Article 12, NIST AI RMF, and financial regulatory frameworks.
GLACIS answers a different question: "Did our controls execute correctly for this specific AI decision?"
Side-by-Side Comparison
| Capability | GLACIS | Holistic AI |
|---|---|---|
| Core Approach | Continuous runtime attestation | Periodic audits and assessments |
| Evidence Type | Cryptographic proof per inference | Assessment reports and documentation |
| Temporal Coverage | Every AI decision (100%) | Snapshot at audit time |
| Bias Testing | Partner integrations available | Core strength, deep expertise |
| Tamper Evidence | Cryptographically secured | Document-based |
| Pre-deployment Assessment | Not primary focus | Core strength |
| Production Monitoring | Continuous, every inference | Periodic reviews |
| Regulatory Mapping | EU AI Act Art. 12, NIST AI RMF, SR 11-7 | NYC LL144, EU AI Act, EEOC |
| Incident Response | Immediate proof of control state | Retrospective investigation |
The Temporal Coverage Gap
Here’s the critical difference that organizations often overlook: what happens between audits?
Point-in-time audits—no matter how thorough—capture a snapshot. If you conduct quarterly bias audits, you have evidence of compliance on four days per year. What about the other 361 days?
The Gap Between Audits
During the 90 days between quarterly audits, your AI system might process millions of decisions. Models can drift. Configurations can change. Controls can fail silently. Point-in-time audits prove compliance existed at audit time—not that it persisted afterward.
This isn’t a criticism of Holistic AI specifically—it’s a limitation of the entire point-in-time audit paradigm. The same gap exists with any periodic assessment approach.
Why the Gap Matters
- Model Drift: Production models evolve. Fine-tuning, prompt changes, and data updates can alter behavior without triggering new audits.
- Configuration Changes: Someone disables a content filter for testing and forgets to re-enable it. The next audit is months away.
- Silent Failures: A guardrail fails to activate due to a bug. Without continuous monitoring, you won’t know until the next assessment—or worse, an incident.
- Regulatory Scrutiny: When regulators or litigators ask "prove your controls were working on March 15th," a Q1 audit report won’t suffice.
When to Use Each Solution
Choose Holistic AI When:
Pre-Deployment Bias Audits
You need rigorous statistical analysis of model fairness before launching. Holistic AI’s bias testing methodologies are well-established and court-tested.
NYC Local Law 144 Compliance
You’re deploying automated employment decision tools in New York City and need the annual bias audit required by law.
Initial Risk Assessment
You’re evaluating AI systems for risk categorization under EU AI Act or building your first AI governance program.
Expert Consulting Needs
You need hands-on guidance from AI ethics and compliance experts for complex regulatory interpretations.
Choose GLACIS When:
Continuous Compliance Evidence
You need to prove controls executed correctly for every AI decision, not just at audit time. Essential for high-risk AI in regulated industries.
Production Runtime Monitoring
You want real-time visibility into whether your AI guardrails and safety mechanisms are actually executing in production.
EU AI Act Article 12 Logging
You need automatic logging capabilities that ensure traceability throughout the AI system lifecycle with tamper-evident records.
Incident Defense Preparation
You want cryptographic proof of control state that can withstand regulatory scrutiny or litigation discovery.
Financial Services Model Risk
You’re subject to SR 11-7 or similar model risk management requirements that demand ongoing validation evidence.
Using Both Together
Here’s what many organizations miss: GLACIS and Holistic AI aren’t competitors—they’re complements.
Consider a mature AI governance program:
A Combined Approach
- Pre-deployment: Use Holistic AI for comprehensive bias testing and risk assessment before launching your AI system.
- Production: Deploy GLACIS to generate continuous attestation evidence that your controls execute correctly for every inference.
- Periodic Review: Use Holistic AI for quarterly or annual deep-dive assessments to catch any systematic issues that continuous monitoring might miss.
- Incident Response: When something goes wrong, GLACIS provides cryptographic proof of exactly what controls were active at the moment of the incident.
This layered approach addresses both the breadth of pre-deployment assessment and the depth of continuous runtime evidence.
The Compliance Lifecycle
| Phase | Best Fit | Why |
|---|---|---|
| Design & Development | Holistic AI | Risk assessment, bias testing, documentation |
| Pre-Deployment Audit | Holistic AI | Comprehensive fairness evaluation |
| Production Operation | GLACIS | Continuous attestation for every decision |
| Periodic Review | Both | GLACIS data informs Holistic AI assessments |
| Incident Investigation | GLACIS | Cryptographic proof of control state |
| Regulatory Submission | Both | Assessment reports + continuous evidence |
Frequently Asked Questions
Is Holistic AI wrong to focus on point-in-time audits?
No. Point-in-time audits serve important purposes—they’re required by some regulations (like NYC LL144), they catch systematic issues, and they provide expert analysis that automated systems can’t replicate. The limitation isn’t with Holistic AI specifically; it’s inherent to the audit paradigm. That’s why continuous attestation complements rather than replaces periodic assessment.
Can GLACIS do bias testing like Holistic AI?
GLACIS focuses on runtime attestation—proving controls executed correctly—rather than statistical fairness analysis. For comprehensive bias testing, we recommend partners who specialize in that domain. GLACIS can attest that bias mitigation controls were active; Holistic AI can assess whether those controls are effective.
Which solution is more expensive?
Pricing models differ significantly. Holistic AI typically charges for assessments and consulting engagements. GLACIS charges for continuous attestation infrastructure based on inference volume. The right comparison isn’t cost-per-solution but cost-per-risk-mitigated. Continuous evidence often costs less than a single regulatory enforcement action or lawsuit.
What if I can only choose one?
Start with your regulatory requirements. If you need NYC LL144 compliance, you need periodic bias audits. If you’re deploying high-risk AI under EU AI Act with Article 12 logging requirements, you need continuous evidence. Many organizations start with one and add the other as their governance program matures.
How quickly can I implement each solution?
Holistic AI assessments can typically be completed in weeks depending on system complexity. GLACIS Evidence Pack deployments can generate production evidence within days. Both timelines depend on your existing documentation, system access, and governance maturity.
Making the Right Choice
The question isn’t "GLACIS or Holistic AI?"—it’s "What compliance gaps do I need to close?"
If your gap is pre-deployment assessment and bias testing, Holistic AI delivers. If your gap is proving controls worked for every production decision, GLACIS delivers. If you’re building a mature AI governance program, you likely need both.
The gap between audits is real. Regulators are increasingly asking not just "were you compliant?" but "can you prove you were compliant on this specific date for this specific decision?" Continuous attestation answers that question in a way periodic audits cannot.