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Counter-UAS Incident Reporting Tool β†’ National Drone-Incursion Intelligence Feed

63/100

Give SLTT police/corrections a free tool to file their newly mandated counter-drone incident reports, then aggregate the de-identified stream into the only national drone-incursion dataset sold to airports, prisons, stadiums, and critical-infrastructure security teams.

Worth deeper research β€” promising but has risk. Β· created 2026-07-12 17:21 UTC

public recordssaasapiindustrialdatalong-termrevisit later

Scorecard

newness 9/10
convergence 8/10
demand evidence 7/10
existing spend 6/10
solo feasibility 6/10
speed to mvp 8/10
speed to revenue 5/10
distribution 5/10
competitive gap 7/10
expansion 8/10
founder fit 7/10

Penalty flags
long trust cycle pii risk (βˆ’6 from raw 69)

Opportunity brief

What changed
On 2026-07-06 DOJ+DHS published an interim final rule codifying the SAFER SKIES Act, granting State/local/Tribal/territorial (SLTT) law-enforcement and correctional agencies brand-new authority to detect and mitigate drones β€” bundled with mandatory incident-reporting obligations (FACT: FedReg 2026-13609). Separately, NY announced $100M in state law-enforcement-technology funding (FACT: Hochul .gov release, signal 2785).
Why now
The authority and its reporting duty are days old; thousands of agencies suddenly have an obligation with no purpose-built tooling. First-mover on the free reporting tool captures the aggregation position before any incumbent notices (HYPOTHESIS on first-mover advantage).
Converging signals
Three signals meet at one point: (1) a new federal rule creating a defined filer class with a reporting duty (regulation); (2) fresh state LE-tech money to pay for such tools, e.g. NY $100M (fedmoney); (3) critical-infrastructure operators (pipelines, per the PHMSA risk-based repair proposed rule 2026-13805) already run data-hungry, risk-based security programs and are plausible buyers of an external threat feed (industrial).
Customer pain
Two distinct pains. (a) Agencies: FACT β€” they now must report C-UAS incidents but have no workflow, no authorized-tech/legal-authority checklist, and risk non-compliance. (b) Infra security teams: HYPOTHESIS β€” they lack any national picture of where drone incursions actually happen and cannot benchmark their own risk. Note: the rule text provided does not specify report contents, frequency, or destination β€” a material unknown.
Who pays
Tool side: agencies, funded by state LE-tech grants (e.g. NY 2785). Data side: critical-infrastructure security teams (pipeline/utility, airports, stadiums), prison systems, and their insurers, for the aggregated incident-intelligence dashboard/feed.
Solved today
HYPOTHESIS (not evidenced in input): agencies would report via spreadsheets, email, or a closed federal portal if one exists; infra teams rely on vendor point-solutions (Dedrone, D-Fend) and ad-hoc airspace data. No national aggregated SLTT-incident dataset is asserted to exist.
Why current solutions are bad
Manual reporting is error-prone and produces no reusable data; point-solution vendors see only their own installed sensors, not a cross-agency national picture. The aggregation gap is the whole thesis.
Proposed product
A free/subsidized web workflow for agencies to capture and export a compliant C-UAS incident/mitigation report (event capture, authorized-tech + legal-authority checklist, export matching the rule's fields). Layered on top: a paid, de-identified geo/time drone-incursion intelligence dashboard and API for infra/venue security teams and insurers.
MVP version
The agency reporting tool alone: a form that mirrors the rule's required reporting fields plus a legal-authority/authorized-equipment checklist, PDF/CSV export, and per-agency account. Ship this before the data product β€” it must earn trust and a data-reuse consent clause.
30-day build
Read the IFR in full and extract the exact reporting fields, destination, and whether third-party capture with a data-reuse right is permissible (this is the KILL TEST). Interview 5 SLTT agencies (lean on fire-service/LE credibility) to confirm the workflow and the data-sharing question. Build the reporting form.
60-day build
Pilot the free tool with 3-5 agencies, prioritizing those sitting on LE-tech grant money (NY first). Secure explicit, written de-identification + data-reuse consent in the terms. Validate export against the rule.
90-day revenue plan
With consent, aggregate de-identified incidents into a geo/time feed; sign 2 design-partner buyers (one prison system, one pipeline/utility security team) for a paid dashboard pilot. Revenue likely from the data side, not the free tool.
Distribution path
Direct outreach to agency chiefs and corrections IT via LE associations and grant-administrator channels; sell the data feed via infra-security and physical-security industry networks and insurers. Demonstrated-value motion, not relationship sales.
Pricing hypothesis
Reporting tool free or grant-funded per-seat (~$0-99/agency/mo to seed adoption). Intelligence feed: $1.5k-6k/mo per infra/venue/prison buyer; API/insurer tier higher. First revenue is the data product.
Technical difficulty
Low-moderate. The reporting form and export are trivially buildable solo. Difficulty is non-technical: securing lawful data-reuse rights and reaching critical mass of contributing agencies for the dataset to have value.
Legal / regulatory risk
Elevated and central. Must verify (1) the IFR permits third-party capture rather than exclusive filing into a closed federal portal; (2) de-identified aggregation of law-enforcement incident data is lawful and consented; (3) no CJIS/sensitive-data handling obligations are triggered. These are gating, not peripheral.
Platform dependency
Low on the tool side β€” no app-store or platform owner can deplatform a gov-adjacent reporting tool. The data moat depends entirely on agency willingness to share, which is a policy/consent dependency, not a platform one.
Founder fit
Strong on shape (regulation forces a filer class; build the submission/compliance layer β€” matches his FMCSA ELDT playbook) and on domain (fire-service/public-safety credibility, public-records fluency). Weaker on the second act: the intelligence-feed buyer (infra/prison security) is a different, longer sale than per-filing SaaS.
Breakout potential
High IF the aggregation moat forms: a genuinely proprietary national drone-incursion dataset is a defensible, expandable asset (airports, stadiums, utilities, insurers, 50-state replication). The moat is contingent on the MUST-BE-TRUE.
Final recommendation
CONDITIONAL PURSUE. The reporting-tool half is a clean forced-buyer/founder-fit play; the profitable half (the national dataset) is entirely gated by one unverified legal fact. Do NOT build past the reporting form until the KILL TEST is answered by reading the IFR and confirming with 5 agencies that third-party capture with a data-reuse right is permitted. If it is barred, ship only the compliance tool as a modest per-seat product and drop the moat thesis.
Next action
Read the full text of FedReg 2026-13609, extract the mandated reporting fields and the report's required destination, and determine whether third-party capture + data-reuse is permitted β€” then validate that answer with 5 SLTT agencies before writing product code.

Kill arguments (adversarial)

Competitors

β€’ Dedrone (link) β€” Incumbent C-UAS detection vendor already inside agencies; could add reporting/analytics and capture the same incident data.
β€’ DroneShield (link) β€” Counter-drone hardware/software vendor with LE relationships; potential fast-follower on compliance tooling.
β€’ D-Fend Solutions (link) β€” C-UAS mitigation vendor serving law enforcement/corrections; adjacent incumbent.

Source citations (facts)

β€’ [Rule] Counter-UAS Authority for SLTT Law Enforcement and Correctional Agencies β€” Interim final rule codifying the SAFER SKIES Act authorizes SLTT agencies to conduct C-UAS operations β€” a new forced-filer class with reporting obligations.
β€’ Governor Hochul Announces $100 Million State Investment in Law Enforcement Technology and Equipment β€” NY committed $100M in state law-enforcement-technology funding β€” a procurement pool that could fund agency purchase of the reporting tool.
β€’ [Proposed Rule] Pipeline Safety: Repair Criteria for Hazardous Liquid and Gas Transmission Pipelines β€” Pipeline operators run risk-based, data-driven integrity programs β€” evidence they are data-buying critical-infrastructure security customers for a threat feed.

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