Research, Trends & Methodology

Opportunity thesis

Why this portal exists

For physical security practitioners, physicalsecurity.AI generates safer prompts, operational playbooks, readiness actions, procurement questions, integration plans, and learning paths so they can move from AI interest to a reviewable next action. The portal is positioned between industry education organizations and product vendors: it does not replace either.

Search demand method

How Google Trends Is Used

Google Trends is a directional editorial tool, not an absolute keyword-volume source. Google samples, anonymizes, aggregates, normalizes, and scales interest from 0 to 100 for the selected geography and period. A value of 100 marks the peak relative interest within that comparison; it does not mean 100 searches.

Fixed monthly watchlist

• Physical security
• Access control systems
• AI security cameras
• Video analytics software
• Visitor management software

The live Google endpoint rate-limited automated retrieval during the August 26, 2026 update, so this site does not publish an unverified current index. The exact five-year U.S. comparison remains available for direct inspection.

Open the live Google Trends comparison
Read Google's normalization and sampling explanation

Tool methods

What the MVP Scores Mean

AI readiness score

The readiness tool averages six project-defined planning inputs: mission criticality, policy maturity, data handling, integration readiness, team literacy, and incident-process maturity. The option weights and 55/70 status thresholds are physicalsecurity.AI heuristics designed to start a discussion. They are not validated risk probabilities, compliance scores, NIST ratings, or proof that a deployment is safe.

Procurement and integration outputs

Procurement matches count selected needs against transparent solution-model tags. Integration outputs use a standards-first planning rule: ONVIF, OSDP, documented APIs, identity integration, test evidence, and exit terms reduce avoidable lock-in, but do not guarantee interoperability in a specific system.

ROI scenario planner

The ROI planner uses conventional project arithmetic with user-supplied assumptions. Annual modeled hours saved equal monthly alerts × 12 × the addressable share × current minutes per alert ÷ 60 × the expected time reduction. Gross labor-capacity value equals those hours × loaded hourly labor cost. First-year ROI equals (gross capacity value − annual service cost − implementation cost) ÷ (annual service cost + implementation cost).

Simple payback solves implementation cost ÷ monthly net capacity value, where monthly net capacity value is gross monthly capacity value minus monthly recurring service cost. Payback is reported as unavailable when recurring cost equals or exceeds modeled gross value. A zero-cost scenario does not receive an ROI percentage because division by zero is not meaningful.

These outputs model redeployable labor capacity, not guaranteed cash savings, headcount reduction, avoided loss, or AI accuracy. Users should rerun one assumption at a time using observed pilot minimum, expected, and maximum values. This sensitivity practice follows the principle in the GAO Cost Estimating and Assessment Guide. AI performance and operational impact also require context-specific measurement, as described by the NIST AI RMF Measure playbook.

Prompt and incident outputs

Prompt templates force separation of facts, assumptions, unknowns, and human approval. Incident playbooks are generic starting points only. Site SOPs, emergency plans, legal duties, training, dispatch rules, and qualified judgment always take priority.

Commercial separation

Affiliate, Advertising & Editorial Policy

• Paid placement never changes a readiness score, shortlist score, prompt, playbook, or editorial conclusion.
• Every paid unit will be labeled “Sponsored” before the user clicks it.
• Every compensated link will be labeled “Affiliate link” beside the link, not only in a footer.
• Vendors cannot buy a “best” ranking. Comparisons must publish use-case fit, evidence, constraints, and update date.
• Survey sponsorship and vendor-authored research are disclosed at the claim.
• Anonymous aggregate analytics may be used later, but raw utility inputs will not be sent as analytics events.

Primary source register

Sources Used for the August 2026 Update

• ASIS 2025 Security Trends Research — AI use, use cases, learning interest, strategic role, and survey methodology.
• SIA Security Market Index — July-August 2026 business-condition signal and prior editions.
• Genetec State of Physical Security 2026 — vendor-sponsored end-user, channel, consultant, and manufacturer survey evidence.
• NIST AI Risk Management Framework — voluntary AI risk-management framework and playbook resources.
• BLS Occupational Outlook Handbook — U.S. employment and openings for guards and gambling-surveillance officers.
• ONVIF and SIA OSDP — interoperability scope and access-control communication standard.
• Google Trends data FAQ — sampling, normalization, scaling, low-volume handling, and statistical noise.
• GAO Cost Estimating and Assessment Guide — assumptions, documentation, uncertainty, and sensitivity analysis.
• NIST AI RMF Measure playbook — context-specific metrics, evaluation, limitations, and ongoing measurement.

Last evidence review: August 26, 2026. Claims should be rechecked at least quarterly or when a source publishes a new edition.

RFP Requirements Builder method

The builder converts selected scope and risk conditions into deterministic requirements. It does not call an AI model, score vendors, or claim legal or standards compliance.

Physical Security AI Companies directory method

The directory is a non-exhaustive, alphabetical source map of representative product categories. Inclusion does not mean endorsement. Profiles summarize vendor-published product information and pair each claim with a limitation or procurement question. The directory does not score vendors, estimate market share, accept paid ranking, or treat a marketing claim as independent proof.

• Inclusion basis: relevance to a practitioner AI workflow, a current primary product source, and contribution to category coverage.
• Evidence label: all current profiles are explicitly labeled vendor-published product information.
• Ordering: alphabetical after local filters; sponsorship cannot change position.
• Review rule: recheck sources quarterly and remove or revise claims that no longer have current support.

Open the company directory and buyer-fit navigator.

AI Physical Security Course Navigator method

The course navigator combines general AI literacy, applied prompting, public governance guidance, association education, and vendor-specific technical training. Plans are deterministic and role-based; they do not rank providers by commission, popularity, or unverified outcomes.

• Core criterion: every recommended resource must support a defined physical-security work product or risk-management capability.
• Commercial separation: Coursera resources are potential affiliate inventory, but all links remain direct and untracked until owner approval and product eligibility are confirmed.
• Authority balance: free NIST guidance and unpaid ASIS, SIA, and Axis links remain in the curriculum even when they cannot generate commission.
• Limit: completion, certificates, ratings, and provider claims do not prove workplace competence; the generated plan requires an applied artifact and human review.

Open the AI Physical Security Course Navigator.

AI Physical Security Jobs & Skills Mapper method

The career mapper treats job titles as unstable labels and models seven role families through observable tasks, skills, and work products. Its deterministic 56-point heuristic uses only four user selections: current background, preferred daily work, applied AI experience, and travel tolerance.

• Scoring: 14 points each for background and preferred-work alignment, four each for AI readiness and travel compatibility, plus a 20-point base.
• Sources: SIA/FAST career pathways inform the physical-security ecosystem; NIST NICE informs the task/knowledge/skill structure; BLS supplies clearly bounded occupational context.
• Limits: the tool does not predict hiring, evaluate credentials, scrape openings, estimate salary, or define “AI physical security” as an official occupation.
• Commercial separation: future sponsored jobs must remain outside organic role-fit output and cannot change scores or guidance.

Open the AI Physical Security Jobs & Skills Mapper.