Federal AI Intelligence · U.S. Government AI Observatory

Custom inventory report

Filter: topic = Cybersecurity; AI type = classical_ml; sourcing = unknown

Generated 2026-10-12 08:44 UTCDataset: 2025 Federal Agency AI Use Case Inventory (individually reported use cases), Office of Management and Budget (OMB). Version 2025-inventory@62d5baa65fa3, processed 2026-10-11, rules 2026.10.1. Static snapshot.Applied filters: topic = Cybersecurity; AI type = classical_ml; sourcing = unknown · 10 records

Key findings

10 use-case records from 5 agencies: 2 deployed (20%), 0 pilots, 8 in development or acquisition, 0 retired, 0 with no stage reported.
2 records are designated high-impact; 0 of them (0%) answered at least one minimum-practice question.
DHS, VA, GSA report 8 of 10 use cases (80%).
0% of reported deployed use cases (0 of 2) are classified as generative or agentic AI.
Sourcing is reported for 0% of use cases. Where reported, n/a involve in-house development.

Lifecycle

Stage	Records	Share
Deployed	2	20%
Pre-deployment	8	80%

AI technology

Classification	Records	Share	Deployed
Classical / predictive ML	10	100%	2

Agencies

Agency	Records	Deployed	High-impact	Completeness
DHS · Department of Homeland Security	3	0	0	58%
VA · Department of Veterans Affairs	3	0	2	58%
GSA · General Services Administration	2	2	0	33%
NASA · National Aeronautics and Space Administration	1	0	0	58%
NEA · National Endowment for the Arts	1	0	0	58%

High-impact designation

Designation	Records
High-impact	2
Presumed high-impact, determined not	1
Not high-impact	5
Not reported	2

Governance evidence: use cases designated high-impact

Practice	No answer	In scope
Pre-deployment testing	2	2
AI impact assessment	2	2
Independent review	2	2
Ongoing monitoring	2	2
Operator training	2	2
Fail-safe	2	2
Appeal process	2	2
User and public consultation	2	2

"In place" means the agency reported the practice as established. "No answer" means the field was blank; it says nothing about whether the practice exists.

Method

All figures are computed directly from the normalised inventory records. Category labels are normalised from the source answer options; original values are kept on each record. Governance answers are classified as in place, in progress, not in place, not applicable, precluded, waived, other or no answer, and blanks are never read as "no". See the Methodology page for full rules.

Limitations

Figures are what agencies reported in the 2025 inventory. They are not verified, and registration of use cases is not complete for every agency.
A blank answer is missing evidence, not evidence that a practice or safeguard is absent.
Counts are use-case records. A larger portfolio does not indicate better performance or greater AI maturity, and agencies differ in how they split systems into use cases.
The inventory has no budget, cost, accuracy or outcome data. None is shown or inferred here.
This is a disclosure analysis. It is not an audit, a compliance determination or an official government assessment.

Source records

All 10 records.

Record	Agency ID	Agency	Use case	Stage
R1179		GSA	Elastic Machine Learning Threat Detection (Phase 1)	Deployed
R1180		GSA	Elastic Machine Learning Threat Detection (Phase 2)	Deployed
R0299	NASA-578	NASA	Autonomous Network Security	Pre-deployment
R0554	NEA-0001	NEA	Managing Cybersecurity Controls	Pre-deployment
R1814	DHS-417	DHS	Machine Learning Analysis Applied to Cyber Threat Hunt Data	Pre-deployment
R1819	DHS-2446	DHS	Cyber Threat Detection	Pre-deployment
R1833	DHS-107	DHS	Malware Reverse Engineering	Pre-deployment
R2068	VA-25-6956	VA	Cyber Authorization Memo Generation	Pre-deployment
R2072	VA-24-585	VA	Forescout: AI Behavioral Anomaly Detection w/ Automated Enforcement	Pre-deployment
R2145	VA-25-6903	VA	Policy Knowledge Base	Pre-deployment

Prepared with Federal AI Intelligence by Irakli Petriashvili. Source: https://github.com/ombegov/2025-Federal-Agency-AI-Use-Case-Inventory