AI Physical Security Procurement Compliance: The 2026 Federal and State Regulatory Framework for Security Directors, Procurement Officers, and AI Vendors
GAO-26-107859, GSA GSAR 552.239-7001, and California EO N-5-26 have reshaped the compliance landscape for every organization deploying AI in physical security — here is what you must know and do before your next procurement cycle.
Three Regulatory Shifts Redefining AI Physical Security Procurement in 2026
In the span of six weeks this spring, three distinct regulatory events rewrote the terms of engagement for every AI vendor seeking government contracts and every government agency buying physical security technology. The GAO published its first detailed audit of AI acquisition failures across federal agencies (GAO-26-107859, April 13, 2026). The General Services Administration issued a draft contract clause — GSAR 552.239-7001 — that would impose sweeping disclosure, data-rights, and risk-documentation requirements on any AI contractor working across the federal schedule. And California Governor Gavin Newsom signed Executive Order N-5-26, directing the state to build a certification framework for AI vendors seeking state procurement contracts within 120 days.
For security directors and risk managers at government-adjacent facilities — hospitals, public universities, transit authorities, state agencies, and municipal government buildings — these developments are not abstract regulatory noise. They define the compliance posture, vendor selection criteria, and contractual risk profile of any AI physical security deployment procured after mid-2026. This briefing is the operational translation layer: what each development requires, what it means for technology procurement decisions, and where the current compliance gaps sit.
What GAO-26-107859 Actually Found: Six Systemic Failures in Federal AI Procurement
The Government Accountability Office examined 13 AI acquisitions across four agencies — the Department of Defense, Department of Homeland Security, General Services Administration, and Department of Veterans Affairs — and documented a consistent pattern: agencies are acquiring AI technology faster than their procurement frameworks can evaluate it, then learning expensive lessons without sharing them with peers. The report, released April 13, 2026, identified six challenge categories that recur across agencies regardless of mission, vendor, or technology type.
First: access to subject matter experts. Agency contracting officers lack the technical depth to evaluate AI proposals. Officials at multiple agencies told GAO that it was difficult to find data scientists, machine learning engineers, or computer vision specialists to review vendor claims during the source selection process. In physical security contexts, this gap is acute: a contracting officer assessing competing gun detection or perimeter AI systems has no institutional basis for evaluating false-positive rates, model drift under adverse conditions, or edge-processing architecture — the very variables that determine operational effectiveness.
Second: data and intellectual property rights. Standard FAR-based contracts were not designed for AI systems that learn, update, and incorporate agency-generated data into model improvements. Agencies are signing contracts without securing rights to their own operational data or governing whether vendors can use detection events, alert outcomes, and system logs to train improved models.
Third: acquisition timelines misaligned with AI development cycles. FAR-based contract awards can take up to two years. AI development cycles iterate in weeks. Department of Veterans Affairs senior leaders told GAO that by the time many agencies move from pilot to production at standard procurement pace, they are purchasing last year's innovation. For physical security AI, this matters because model capabilities against concealed versus drawn weapons, in low-light versus daylight conditions, or across crowded versus sparse environments advance materially on annual timescales.
Fourth: requirements definition. Agencies struggle to write technical requirements specific enough to be evaluated objectively. Vague performance specifications — "detect threats in real time" or "reduce false positives" — cannot be evaluated against competing vendors, cannot be audited post-deployment, and cannot form the basis for contract remedies when performance falls short.
Fifth: testing and continuous evaluation. AI systems degrade over time as the physical environment changes — lighting shifts, personnel changes uniforms, camera angles are modified. Agencies are not systematically building post-deployment performance evaluation into their contracts, leaving no mechanism to detect model drift or to hold vendors accountable for detection accuracy at month 18 of a three-year contract.
Sixth: pricing opacity. AI pricing structures — platform fees, inference compute costs, model update fees, integration charges — are opaque enough that agencies cannot validate whether they are receiving comparable value across competing bids. The GAO found that officials consistently said they found it hard to understand AI-related costs.
The GAO recommended that all four agencies update departmental policies to require systematic collection and submission of AI acquisition lessons learned to the GSA-managed repository, with DHS setting a target completion date of July 31, 2026 and the VA setting August 1, 2026. Congress appropriated approximately $1.7 billion for federal AI efforts, and the GAO's concern is that those funds are being deployed through procurement processes not yet designed to evaluate what they are buying.
The GSA GSAR 552.239-7001 Clause: What AI Physical Security Vendors Must Disclose
On March 6, 2026, the GSA Federal Acquisition Service released GSAR 552.239-7001, "Basic Safeguarding of Artificial Intelligence Systems" — a draft contract clause that would be inserted into all solicitations and contracts for AI capabilities across the federal schedule if adopted. The comment period closed April 3, 2026. The clause is under consideration for inclusion in Refresh 32 of the Multiple Award Schedule.
The clause imposes six core obligations on AI contractors — obligations that are highly material for physical security AI vendors:
GSAR 552.239-7001 Core Obligations for AI Physical Security Vendors
| Obligation | Requirement | Physical Security Implication |
|---|---|---|
| AI Disclosure | Disclose all AI systems used in contract performance within 30 days of award, including any modifications made to comply with foreign government or commercial regulatory frameworks | Gun detection, perimeter AI, fall detection, and loitering models must all be itemized; foreign-trained models (EU AI Act compliant variants) require explicit disclosure |
| Model Provenance Statement | Submit a documented model provenance statement covering training data sources, fine-tuning datasets, and model architecture lineage | Vendors must be able to certify the origin and composition of training datasets used for detection models — including whether government facility footage was used for training |
| Risk Assessment | Provide a model-specific risk assessment with documented mitigation measures | Physical security AI must document failure modes, false-positive and false-negative risk under operational conditions, and mitigation architecture (on-premises processing, human-in-the-loop alert routing) |
| Data Rights | Government receives ownership of all data inputs, outputs, and custom developments; vendors prohibited from using government data for model training or improvement | Alert logs, detection event data, camera feed metadata, and system performance telemetry generated at government facilities belong to the government — not the vendor |
| Government Use Rights | Government may use the AI system for any lawful government purpose | Vendors cannot contractually restrict how agencies deploy detection outputs, creating tension with vendor-specific use-case restrictions in standard commercial licenses |
| Supply Chain Responsibility | Prime contractors are responsible for compliance of any service provider whose AI system is used in contract performance, even if that service provider is not a direct party | A physical security integrator using a third-party AI detection engine is fully liable for that engine's GSAR compliance — due diligence on subcontractor AI components becomes a contractual obligation |
The U.S. Chamber of Commerce, in formal comments submitted before the April 3 deadline, raised concerns about the breadth of government use-rights and the data ownership provisions, noting that they could deter commercial AI vendors from entering the federal market. Trade groups similarly warned that the supply chain responsibility provisions would create compounding compliance costs throughout the integrator ecosystem.
For procurement officers evaluating AI physical security vendors, the GSAR clause — whether adopted in its current form or modified through the Refresh 32 process — signals the direction of federal contracting. Vendors who cannot produce model provenance documentation, cannot segregate government facility data from commercial training pipelines, or cannot produce a model-specific risk assessment are already behind the compliance curve for serious federal procurement competition.
The GSAR Clause and the Anthropic-Pentagon Dispute: What the Backdrop Means
The government use-rights provision in GSAR 552.239-7001 — granting agencies permission to use AI for "any lawful government purpose" — followed an explosive procurement dispute between Anthropic and the Department of Defense in early 2026 over use-case restrictions in commercial AI licenses. The dispute surfaced a tension that physical security vendors must understand: commercial AI licenses typically contain use-case restrictions designed for liability and ethical risk management, while the government assumes it holds sovereign authority to deploy procured technology without vendor-imposed operational limits.
For physical security AI specifically, this means the government use-rights framing is not primarily about surveillance overreach — it is about procurement officers insisting they can integrate detection outputs into any lawful operational workflow, including ones the vendor did not anticipate at contract execution. Vendors with narrowly scoped commercial licenses should review those instruments against the GSAR draft before bidding on federal schedules.
California Executive Order N-5-26: The First State-Level AI Vendor Certification Framework
On March 30, 2026, California Governor Gavin Newsom signed Executive Order N-5-26, directing the California Department of General Services and the Department of Technology to develop new AI vendor certification requirements within 120 days. The Order positions California as the first state to build a systematic certification and procurement compliance framework for AI vendors seeking state government contracts — a market that, given California's size, represents procurement scale comparable to many federal agency programs.
The Order has five operational components that matter directly for AI physical security vendors:
Vendor certification attestations. New certifications will require vendors to attest to their policies and safeguards concerning exploitation of illegal content, harmful model bias, and violations of civil rights and civil liberties. For physical security AI, the civil rights and civil liberties provision is the critical vector: any detection system operating in a state facility context must be able to document that its models do not produce disparate detection outcomes across demographic groups — a standard that requires bias testing data most current commercial physical security AI vendors have not publicly released.
Supply chain risk review. The Order directs the Department of Technology's Chief Information Security Officer to review federal designations of companies as supply chain risks. In the aftermath of the Anthropic-Pentagon dispute, this provision gives California authority to independently assess whether federally contracted AI vendors pass state-level security standards — including for physical security technology used in state-owned facilities.
AI watermarking guidance. The Department of Technology must issue guidance on watermarking AI-generated or significantly manipulated images or video. For physical security platforms that generate detection overlays, confidence score annotations, or alert imagery, this provision may introduce documentation requirements for AI-generated visual outputs used in incident investigation workflows.
Contractor responsibility reforms. Certification recommendations will include reforms to contractor responsibility determinations — the evaluation process by which agencies assess whether a vendor is qualified to receive a contract award. AI vendors with inadequate safety documentation, unresolved bias testing gaps, or opaque model provenance may face exclusion from state procurement competitions on responsibility grounds rather than price or technical grounds.
120-day timeline pressure. The 120-day window from March 30, 2026 means draft certification standards should emerge by late July 2026. Vendors pursuing California state contracts — including those providing AI physical security to state universities, state-operated healthcare facilities, transit authorities, and state government buildings — should have compliance documentation in preparation now, not after certification standards are published.
Three 2026 Regulatory Frameworks Governing AI Physical Security Procurement
Authority, scope, timeline, and key vendor obligations at a glance.
- DHS lessons-learned submission: July 31, 2026
- VA submission deadline: August 1, 2026
- 6 challenge categories agencies must document
- Applies to DOD, DHS, GSA, VA AI acquisitions
- Drives requirements for technical evaluator access
- Post-deployment performance evaluation standard
- AI disclosure within 30 days of contract award
- Model provenance statement required
- Model-specific risk assessment required
- Government data ownership — all inputs and outputs
- No vendor training on government data
- Prime contractor liable for subcontractor AI compliance
- AI vendor certification attestations
- Civil rights and bias documentation required
- Supply chain risk CISO review
- AI watermarking guidance for visual outputs
- Contractor responsibility reform for AI vendors
- 120-day framework development timeline
DHS Surveillance Expansion: What the $18 Billion Technology Procurement Surge Means for AI Vendors
While the GAO and GSA activity addresses procurement process reform, the Department of Homeland Security is simultaneously executing the largest physical security AI procurement expansion in its history. Equipment and technology spending at DHS is projected to increase from approximately $11 billion to $18 billion annually, with AI-powered surveillance systems representing a significant portion of that growth.
The agency is building out a network of 890 AI-powered autonomous surveillance towers along the southern border, layering detection, tracking, and interdiction capabilities into a single operational grid. The DHS Modular Mobile Surveillance System (M2S2) program — which fuses AI, radar, high-powered cameras, and wireless networking into vehicle-mounted units — is expected to result in purchase agreements of up to 10 years' duration. Airship AI received a $2.1 million DHS border surveillance contract in early 2026. The Scylla AI and Carahsoft partnership announced in the same period targets AI-powered video analytics deployment across federal facilities.
The procurement surge creates a specific opportunity and compliance tension simultaneously. Vendors who can meet the emerging GSAR 552.239-7001 requirements — model provenance documentation, risk assessments, government data ownership compliance — will have a structural procurement advantage over those who cannot. Vendors who have not built their compliance documentation architecture will face barriers at precisely the moment when federal demand is at its highest.
For physical security AI vendors with DHS SAFETY Act designation, the compliance stack is partially pre-built. The SAFETY Act Full Designation process already requires technology performance documentation, operational testing records, and capability claims support — the same categories that GSAR 552.239-7001 would formalize into contract requirements. The DHS SAFETY Act designation and certification briefing covers the relationship between SAFETY Act tiers and procurement credentialing in detail.
What These Frameworks Mean for Security Directors at Government-Adjacent Facilities
The regulatory pressure does not land exclusively on vendors. Security directors at hospitals, public universities, transit authorities, municipal government buildings, and state-licensed healthcare facilities are downstream of these frameworks in two ways: as procurement officers selecting vendors, and as operators of facilities that may themselves be subject to state AI procurement standards if they receive government funding or operate under government oversight.
Public Universities and Higher Education
State universities in California procuring AI physical security technology for campus safety will operate inside the Executive Order N-5-26 certification framework once standards are published. Security directors should be verifying now whether current or prospective AI vendors can produce the attestation documentation the EO will require — particularly civil rights and bias testing records. See the Higher Education Physical Security 2026 Sector Playbook for the full campus deployment framework.
Federal Agency and Government Building Operators
Facilities managers and security directors at federal agencies procuring AI physical security systems are working inside the GSAR 552.239-7001 draft framework now. The comment period has closed; the clause is under consideration for Refresh 32 inclusion. Any AI physical security procurement initiated after mid-2026 should include GSAR compliance documentation requirements in the solicitation, even before formal adoption, to ensure selected vendors are compliant at contract execution.
Hospital and Healthcare System Security
Healthcare facilities receiving federal funding operate under a procurement overlay from CMS, HRSA, and in some cases DHS SAFETY Act requirements. The GSAR clause's data ownership provisions are particularly relevant for healthcare: AI physical security platforms that process video feeds near patient care areas must not only comply with HIPAA's requirements on PHI — they must now also structure their data architecture so that government facility video data is firewalled from vendor training pipelines. The Healthcare Workplace Violence AI Detection Playbook covers the intersection of clinical privacy and AI detection architecture.
Transit Authorities and Infrastructure Operators
Public transit authorities deploying AI perimeter detection, loitering detection, or weapon detection across stations and yards typically operate on federal grant funding with associated procurement compliance obligations. The GAO's finding on requirements definition is directly applicable: transit security RFPs for AI technology must include specific, auditable performance standards — detection accuracy thresholds, false-positive rate tolerances, maximum alert latency — to avoid the vague-specification failure mode documented across all four agencies in GAO-26-107859.
State-Licensed Financial Services and Insurance
Financial institutions under state charter or state regulatory oversight may find that California EO N-5-26 certification standards apply to AI security systems operating in their facilities — particularly if those systems use models trained on state-regulated data or are procured through state contract vehicles. The EO's contractor responsibility reform provision could affect vendor eligibility determination beyond pure state agency procurement. The insurer AI underwriting intelligence briefing covers the parallel insurance-market compliance pressure on the same vendors.
Manufacturing, Warehouse, and Critical Infrastructure
Facilities operating under DHS critical infrastructure sector designations — energy, water, transportation, food and agriculture — may find that the DHS surveillance technology expansion creates both procurement opportunity and compliance burden. As DHS builds out its AI-powered surveillance infrastructure, critical infrastructure owners who co-deploy AI detection platforms compatible with federal interoperability frameworks may gain procurement preference. OSHA's General Duty Clause enforcement context is covered in the OSHA General Duty Clause enforcement briefing.
The Compliance Documentation Stack: What AI Physical Security Vendors Must Be Able to Produce
Taken together, the three 2026 regulatory frameworks define a compliance documentation stack that any AI physical security vendor pursuing government contracts — federal or California state — must be able to produce on demand. The following represents the minimum documentation set implied by the current regulatory trajectory, not a guarantee of compliance under any specific contract instrument.
AI Physical Security Compliance Documentation Stack — 2026
| Document | Required By | What It Must Contain |
|---|---|---|
| Model Provenance Statement | GSAR 552.239-7001 (proposed) | Training dataset origins, fine-tuning data sources, architecture lineage, any foreign regulatory compliance modifications |
| Model-Specific Risk Assessment | GSAR 552.239-7001 (proposed) | Documented failure modes, false-positive and false-negative rates under operational conditions, adversarial and occlusion performance, mitigation measures |
| AI System Disclosure Register | GSAR 552.239-7001 (proposed) | Itemized list of all AI components used in contract performance, including subcontractor and third-party models |
| Data Architecture Certification | GSAR 552.239-7001 (proposed) | Documentation that government facility data is not used for model training or vendor improvement; data flow diagrams showing government data isolation |
| Bias and Civil Rights Testing Records | California EO N-5-26 (pending standards) | Detection outcome analysis by demographic category; documentation that model performance does not produce disparate outcomes across protected characteristics |
| AI Acquisition Lessons Documentation | GAO-26-107859 (agency compliance) | For agency-side compliance: post-deployment performance records, lessons submitted to GSA-managed repository, testing and evaluation protocols |
| DHS SAFETY Act Designation | Federal procurement preference | Current Full Designation or Certification as a Qualified Anti-Terrorism Technology; documentation of designation scope and covered technology |
IntelliSee holds DHS SAFETY Act Full Designation as a Qualified Anti-Terrorism Technology. The platform processes video on-premises — no cloud transmission of government facility video, and no mechanism for government detection event data to enter vendor training pipelines — which directly addresses the GSAR data architecture requirements. For procurement officers conducting due diligence on AI physical security vendors against this framework, the architectural questions to ask are: where does video processing occur, does detection data leave the facility, and can the vendor produce a model provenance statement covering its detection models?
Writing Compliant AI Physical Security RFPs After GAO-26-107859
The GAO's requirements-definition finding has a direct procurement-writing implication. Security directors issuing RFPs for AI physical security technology should include four categories of measurable performance standards that the GAO found consistently missing in federal acquisitions. First: detection accuracy specifications — minimum confidence thresholds, false-positive rate tolerances, and false-negative rate tolerances under defined test conditions (daylight, low-light, crowded environment, occlusion). Second: alert latency specifications — maximum time from detection event to alert delivery to defined response workflows, measured end-to-end. Third: model maintenance obligations — how the vendor will handle model updates, what performance guarantees carry through updates, and what testing documentation is required before update deployment. Fourth: post-deployment evaluation — quarterly or semi-annual performance reporting against the baseline specifications established at contract award. Without these four elements, a physical security AI contract cannot be meaningfully audited, and the vendor has no contractual obligation to maintain performance beyond initial deployment.
Privacy Architecture as a Compliance Asset: Why On-Premises Processing Matters for Procurement
Across all three 2026 regulatory frameworks, a common thread runs: the question of where AI processing occurs and who owns the data it generates. This is not a privacy-advocacy concern — it is a procurement compliance requirement that vendors must meet and that procurement officers must verify.
The GSAR 552.239-7001 data ownership provisions assume a world where vendors could, in the absence of contractual restraint, use government facility detection data to improve their commercial models. The clause's prohibition on vendor training on government data is only enforceable if there is a documented data architecture that makes such use detectable. On-premises processing — where video never leaves the facility network — is the architectural control that makes the GSAR data-ownership commitment credible rather than aspirational.
California EO N-5-26's civil rights and civil liberties framing similarly favors vendors who can document that their detection models operate on object-level and motion-level pattern recognition rather than biometric identification. A detection system that identifies a drawn firearm based on visual pattern — rather than cross-referencing against a biometric database — is structurally incapable of the civil liberties violations the EO is designed to prevent. This is not an interpretation: it is the architectural distinction the certification framework will need to operationalize.
For security directors evaluating vendors against this framework, the relevant questions are: does the system process video on-premises or in the cloud; does the vendor have access to detection event logs from government facilities; and does the detection modality rely on biometric identification or on object/pattern recognition? Vendors who cannot answer all three questions definitively are not procurement-compliant with the 2026 regulatory trajectory — regardless of whether GSAR 552.239-7001 is formally adopted on schedule.
The AI Gun Detection Failure Modes threat intelligence analysis covers the technical architecture questions that belong in vendor evaluation — including the detection accuracy variables that a compliant requirements specification must reference.
Frequently Asked Questions: AI Physical Security Procurement Compliance in 2026
Does GSAR 552.239-7001 apply to AI physical security products already deployed at federal facilities?
The GSAR 552.239-7001 draft clause applies to new solicitations and contracts, not retroactively to existing deployments. However, agencies following the GAO's recommendations to update procurement policies may incorporate similar requirements into contract modifications and renewals. Vendors with existing federal deployments should assess their compliance documentation readiness now — both for renewal negotiations and in anticipation of the clause's likely adoption through the GSA Refresh 32 process.
What does "model provenance statement" mean in practice for a gun detection system?
A model provenance statement for an AI gun detection system documents where the training data came from (public datasets, proprietary collection, synthetic generation), whether the model was fine-tuned on operational footage and from which facilities, and whether any components were modified to comply with foreign regulatory frameworks such as the EU AI Act. It is a chain-of-custody document for the AI model itself — analogous to a material safety data sheet for a chemical product. Vendors who cannot produce this documentation have a gap that will become a procurement disqualifier under GSAR 552.239-7001 if adopted.
How should a public university in California prepare for EO N-5-26 certification requirements?
California's 120-day timeline runs from March 30, 2026, meaning draft certification standards should emerge around late July 2026. Public universities should use the intervening period to audit current AI physical security vendor contracts for civil rights compliance documentation, bias testing records, and data architecture certifications. Any AI security procurement initiated after July 2026 should include a certification compliance clause requiring vendors to meet or exceed EO N-5-26 standards as a contract condition. The Higher Education Physical Security 2026 Sector Playbook covers the campus deployment context in detail.
What is the relationship between DHS SAFETY Act designation and GSAR 552.239-7001 compliance?
DHS SAFETY Act Full Designation is not a substitute for GSAR 552.239-7001 compliance, but it is a significant compliance enabler. The SAFETY Act designation process requires vendors to document technology capabilities, operational testing records, and performance claims — much of the same documentation infrastructure that GSAR 552.239-7001 would require in a model risk assessment and provenance statement. Vendors with Full Designation have a documentation foundation that reduces the marginal cost of GSAR compliance preparation. Procurement officers should treat SAFETY Act designation as a signal of documentation readiness without treating it as GSAR compliance by itself.
Does on-premises AI processing automatically satisfy the GSAR data ownership requirements?
On-premises processing is the strongest architectural control for GSAR data ownership compliance, but it is not automatic satisfaction. The clause requires vendors to certify that they are not using government facility data for model training or improvement — a commitment that requires both architectural controls (no cloud transmission of detection data) and contractual prohibitions (explicit data use restrictions in vendor agreements). Procurement officers should verify both the architecture and the contractual framework, not assume that one satisfies the other.
What do the GAO's six procurement challenge categories mean for writing an AI physical security RFP?
The six GAO challenge categories translate into six RFP design requirements. Requirements definition: include specific, measurable detection performance standards. Subject matter experts: designate a technical evaluator with AI or computer vision background for proposal review. Data rights: include explicit government ownership of all detection event data generated during the contract. Timelines: use OTA or other flexible procurement mechanisms for pilot phases to avoid FAR-timeline delays. Testing and evaluation: build quarterly performance reporting requirements into the contract. Pricing: require all-in lifecycle cost documentation including compute, update fees, and integration support costs.
Will the GSAR 552.239-7001 clause affect commercial integrators who resell AI security platforms to federal clients?
Yes, and materially so. The clause imposes supply chain responsibility on prime contractors — meaning an integrator who resells a third-party AI detection engine is fully liable for that engine's compliance with all GSAR requirements, even if the AI vendor is not a direct party to the federal contract. Integrators should conduct formal GSAR compliance due diligence on every AI component in their physical security stack before bidding on federal solicitations. The clause effectively converts vendor compliance readiness into a go/no-go criterion for integrator federal eligibility.
Continue the Research
This briefing covers the federal and state procurement compliance framework for AI physical security in 2026. For the operational and technical context that belongs alongside compliance due diligence:
- The DHS SAFETY Act in AI Security: Designation, Certification, and What It Actually Means — the full treatment of SAFETY Act tiers, the QATT designation process, and how designation interacts with procurement credentialing.
- Federal and State Grant Funding for AI Physical Security: The 2026 Procurement Intelligence Briefing — the companion piece covering grant programs, eligible uses, and application timelines for funding AI physical security deployments.
- The EU AI Act and Physical Security AI: A Compliance Intelligence Briefing — for vendors operating across both US and EU government markets, the parallel compliance framework that shapes model provenance and prohibited-use documentation on the European side.
- State-by-State AI Security Legislation: Q2 2026 Tracker — the actively updated tracker of state-level AI legislation with physical security implications, including EO N-5-26 and pending state bills beyond California.
- How IntelliSee works — the technical architecture overview covering on-premises processing, detection modalities, and the data flow design that addresses GSAR data ownership requirements.
More intelligence like this
New IntelliSee research drops monthly at most. Subscribe and get the next sector playbook, technology briefing, or threat intelligence report in your inbox the day it ships.
Request a Compliance Assessment
Talk to an IntelliSee security specialist. No sales pitch — a structured conversation about your environment, your threat profile, and whether computer vision is the right fit.
Request a Risk Assessment