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ARXIV:2604.06148 · AI GOVERNANCE · SUBMITTED 08 APR · 03:22 UTC · FRESHNESS UNKNOWN
ARXIV:2604.06148AI GOVERNANCESUBMITTED 08 APR · 03:22 UTCFRESHNESS UNKNOWNAndrew Kurtz · Klaudia Krawiecka · arXiv
A taxonomy and framework for governing machine identities in AI systems to mitigate enterprise and geopolitical risks.
Opportunity summary
Pain A taxonomy and framework for governing machine identities in AI systems to mitigate enterprise and geopolitical risks.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A taxonomy and framework for governing machine identities in AI systems to mitigate enterprise and geopolitical risks. AI agents, service accounts, API tokens, and automated workflows now outnumber human identities in enterprise environments by…
The governance of artificial intelligence has a blind spot: the machine identities that AI systems use to act. AI agents, service accounts, API tokens, and automated workflows now outnumber human identities in enterprise environments…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. A four-phase implementation roadmap translates the MIGT into actionable enterprise programs.
AI Governance moved forward this cycle; last verified April 2026. Public score 2.0/10.
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A taxonomy and framework for governing machine identities in AI systems to mitigate enterprise and geopolitical risks.
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10.48550/arXiv.2604.06148A taxonomy and framework for governing machine identities in AI systems to mitigate enterprise and geopolitical risks.
Abstract
The governance of artificial intelligence has a blind spot: the machine identities that AI systems use to act. AI agents, service accounts, API tokens, and automated workflows now outnumber human identities in enterprise environments by ratios exceeding 80 to 1, yet no integrated framework exists to govern them. A single ungoverned automated agent produced $5.4-10 billion in losses in the 2024 CrowdStrike outage; nation-state actors including Silk Typhoon and Salt Typhoon have operationalized ungoverned machine credentials as primary espionage vectors against critical infrastructure. This paper makes four original contributions. First, the AI-Identity Risk Taxonomy (AIRT): a comprehensive enumeration of 37 risk sub-categories across eight domains, each grounded in documented incidents, regulatory recognition, practitioner prevalence data, and threat intelligence. Second, the Machine Identity Governance Taxonomy (MIGT): an integrated six-domain governance framework simultaneously addressing the technical governance gap, the regulatory compliance gap, and the cross-jurisdictional coordination gap that existing frameworks address only in isolation. Third, a foreign state actor threat model for enterprise identity governance, establishing that Silk Typhoon, Salt Typhoon, Volt Typhoon, and North Korean AI-enhanced identity fraud operations have already operationalized AI identity vulnerabilities as active attack vectors. Fourth, a cross-jurisdictional regulatory alignment structure mapping enterprise AI identity governance obligations under EU, US, and Chinese frameworks simultaneously, identifying irreconcilable conflicts and providing a governance mechanism for managing them. A four-phase implementation roadmap translates the MIGT into actionable enterprise programs.
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Proof status
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What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
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Preparing verified analysis
Dimensions overall score 2.0
PROBLEM
A taxonomy and framework for governing machine identities in AI systems to mitigate enterprise and geopolitical risks. AI agents, service accounts, API tokens, and automated workflows now outnumber human identities in enterprise environments by ratios exceeding 80 to 1, yet no i...
METHOD
The governance of artificial intelligence has a blind spot: the machine identities that AI systems use to act. AI agents, service accounts, API tokens, and automated workflows now outnumber human identities in enterprise environments by ratios exceeding 80 to 1, yet no integrate...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. A four-phase implementation roadmap translates the MIGT into actionable enterprise programs.
WHY NOW
AI Governance moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A taxonomy and framework for governing machine identities in AI systems to mitigate enterprise and geopolitical risks. AI agents, service accounts, API tokens, and automated workflows now outnumber human identities in enterprise environments by ratios exceeding 80 to 1, yet no integrated framework exists to govern them.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
The governance of artificial intelligence has a blind spot: the machine identities that AI systems use to act. AI agents, service accounts, API tokens, and automated workflows now outnumber human identities in enterprise environments by ratios exceeding 80 to 1, yet no integrated framework exists to govern them.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. A four-phase implementation roadmap translates the MIGT into actionable enterprise programs.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
AI Governance moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A taxonomy and framework for governing machine identities in AI systems to mitigate enterprise and geopolitical risks.
Segment
AI Governance
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Commercial read
2.0/10 public viability
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status
missing
reason
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proof status
unverified
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No verified cost estimate
confidence low
next verification path
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Build readiness
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Artifact maturity
GitHub and Hugging Face maturity payloads
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unknown
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Technical feasibility
partial
Current read
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Gaps
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Run minimal reproduction from the Build Passport prototype path.
Market urgency
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Evidence
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Buyer clarity
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Defensibility
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Integration burden
missing
Current read
No public implementation surface observed.
Evidence
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Gaps
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Write integration checklist from prototype path and target workflow.
Capital intensity
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Regulatory load
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Gaps
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Classify regulatory flags before commercialization planning.
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Paper authors are not treated as operators without consent.
People
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Prototype owner missing.
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Regulatory need unclassified.
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ARTIFACTS
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DEFENSIBILITY
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