The AI risk in your portfolio is not in the model.
It is in the human architecture no one audited.
Falkovia provides AI governance diligence for venture capital and private equity firms. We surface the human architecture risk that standard technical diligence never examines, pre-acquisition, and build the governance infrastructure that protects value creation post-close. Confidential, fixed-scope engagements scoped to deal timelines.
The problem no one is pricing
Venture capital and private equity firms evaluate AI investments across technology, market fit, and team. What they rarely evaluate is the human governance architecture that determines whether AI creates value or creates liability inside a portfolio company. This is not a compliance concern. It is a value creation variable.
The data from the last 24 months is unambiguous: AI does not fail because of code. It fails because the human systems surrounding it were never designed. Decision rights, accountability structures, workforce readiness, and governance architecture are the variables that determine whether AI investment produces value or produces liability.
For an investment firm, this means the AI value creation assumptions embedded in a deal model are functionally dependent on a variable, human governance architecture, that standard technical diligence does not examine.
$4.63M
avg. shadow AI breach cost
Regulatory Exposure Compounds Quietly
State AI laws are live and expanding. Colorado, Texas, and New York create specific liability for organizations without documented human oversight of AI-influenced decisions. Most portfolio companies cannot produce the documentation a regulator would require.
70-85%
of AI initiatives underperform
Adoption Stalls Where It Matters Most
Not because technology breaks, but because trust was assumed, authority was unclear, and the workforce resisted in ways that looked like compliance but functioned as sabotage. The value creation thesis never materialized.
17%
higher cost vs. standard breach
Exit Timelines Extend
Portfolio companies that cannot demonstrate AI governance maturity face additional scrutiny, longer diligence cycles, and valuation discounts at exit. Governance that exists is a defensible asset. Governance that does not exist is a discovered liability.
Engagement Models
AI Governance Due Diligence
Know the liability before you price it.
- •Shadow AI Exposure Assessment
- •Decision Authority Architecture Review
- •Governance Maturity Scoring (Exposed to Architected)
- •Identity Threat Profile
- •Regulatory Defensibility Gap Analysis
- •Documented liability estimate and remediation roadmap
Portfolio Governance Architecture
Build the asset before you need to defend it.
- •G.U.A.R.D. Framework, sector-specific and stress tested
- •Human Authority Lines for every high-risk AI workflow
- •Shadow AI Audit, exposure mapped and governed
- •First 90 Minutes Incident Response Protocol
- •AI Governance Due Diligence Checklist at baseline
- •Board-ready governance documentation, regulator-defensible
Why this matters now
Regulatory acceleration
1,208 AI bills were introduced across 50 states in 2025, with 145 enacted into law. Colorado's AI Act takes effect June 30, 2026. Texas TRAIGA is live. The EU AI Act classifies multiple sectors as high-risk. For portfolio companies deploying AI in any regulated context, compliance is no longer a future roadmap item.
The ROI gap is a governance gap
Organizations with formal AI governance councils reach ROI in 7.5 months compared to 13.5 months without. Successful AI projects allocate 47% of budget to foundations (data, governance, change management) versus 18% in failed projects. Governance is not a cost center. It is the mechanism that converts AI investment into returns.
Shadow AI is a portfolio-wide exposure
59% of employees use unapproved AI tools. Among executives, 93%. The average shadow AI breach costs $4.63M, 17% above standard. 86% of organizations are blind to their own AI data flows. This exposure exists inside your portfolio companies today, whether or not it appears in diligence materials.
Five questions every investment partner should be able to answer
Portfolio AI exposure
Do you know which portfolio companies are using AI, how, and under what governance? Can you map AI adoption across your portfolio and identify where governance architecture is absent?
Decision authority
In each portfolio company, who holds authority over AI decisions: approval, restriction, override, and prohibition? Is that documented, or assumed?
Diligence gaps
Does your standard technical diligence examine human governance architecture (decision authority, oversight structures, accountability mapping), or only the technology stack?
Regulatory readiness
Could your portfolio companies produce AI governance documentation if a regulator asked tomorrow? Would that documentation demonstrate the institutional oversight that state and federal regulators are now requiring?
Value protection
Is AI creating value in your portfolio, or creating undocumented liability that will surface at exit? Can your portfolio companies demonstrate governance maturity to a future acquirer?
Who this engagement serves
Managing Partners and General Partners
Accountable for portfolio-level risk and responsible for ensuring AI adoption across portfolio companies does not create regulatory, reputational, or valuation exposure that reaches the investment committee.
Operating Partners and Portfolio Operations
Responsible for operational value creation and accountable for ensuring AI-driven efficiency gains do not introduce governance gaps that undermine the value creation thesis.
Deal Teams and Due Diligence Leaders
Conducting technical and operational diligence on acquisition targets and responsible for identifying AI governance risk before the deal closes.
Portfolio Company CEOs and COOs
Leading organizations where AI adoption is accelerating and accountable for governance architecture that protects the company from regulatory, legal, and operational exposure.
Investment Committee Members
Exercising oversight of investment decisions and responsible for understanding whether AI governance risk has been adequately examined and addressed.
The technical stack is 10%. The human architecture is 90%. Most never examine it.
Frequently Asked Questions
Technical diligence evaluates the model: does it perform, is the data pipeline sound, are there security gaps. Falkovia evaluates the human governance architecture surrounding the model: who has documented authority over its outputs, what happens when the AI is wrong, whether decisions the AI is now making were ever consciously delegated by a human. Technical diligence misses the layer that turns AI capability into post-acquisition liability.
Yes. Pre-acquisition diligence is typically scoped to 4-6 weeks and structured to align with IC timelines. The deliverable is built to be IC-presentable, with a clear governance risk assessment, exposure quantification, and integration considerations the IC needs to price the deal accurately.
Both. Pre-acquisition produces a governance risk assessment. Post-acquisition produces the governance architecture the portfolio company needs to operate without creating ongoing exposure. Many engagements span both phases, with continuity of context that a new vendor would not have.
Healthcare, higher education, and other regulated sectors where AI adoption is creating governance gaps faster than internal compliance can absorb them. The work is sector-specific because regulatory exposure varies meaningfully by industry, and a generic governance framework will miss the dimensions that actually drive risk.
Every engagement is confidential. Falkovia does not work simultaneously with direct competitors in the same sub-sector without explicit written consent from all parties. Engagement scoping includes a conflict review before any substantive work begins.
Both models are available. Some firms engage Falkovia for diligence only, then turn over the governance roadmap to the portfolio company's internal team. Others retain Falkovia to build the governance architecture post-close, often through a fractional Chief AI Officer arrangement. The structure is scoped to deal complexity and portfolio company readiness.
Next Step
The governance question is whether you know the risk before you price it.
Every engagement begins with a confidential conversation about what your portfolio actually needs.
Start a Confidential Conversation