In today’s digital age, law enforcement agencies are increasingly adopting AI technologies to enhance operations, automate routine tasks, and conduct high-level data analytics to improve efficiency and effectiveness in delivering police services.
However, deciding how and where to deploy AI-driven software solutions — whether on-premises, in a multi-tenant Software-as-a-Service (SaaS) environment, in a government or private cloud, or as part of a hybrid approach — is a complex decision. Each deployment model comes with distinct advantages, challenges, and security responsibilities that police executives must carefully evaluate.
Adding to this complexity, AI-specific considerations such as data privacy, algorithm transparency, and compliance with laws governing personally identifiable information (PII) and law enforcement-sensitive data require careful scrutiny [1]. Making the right choice demands a clear understanding of how each model impacts security, accessibility, and long-term operational needs.
To simplify these concepts for non-IT professionals, this article uses housing analogies to help police executives understand the key differences between software deployment models. Just as selecting a home involves evaluating security, accessibility, and ownership responsibilities, choosing the right software deployment model requires assessing risks, control and compliance factors.
Multi-tenancy SaaS: The apartment rental model
Multi-tenancy SaaS operates like an apartment building. Multiple agency users (tenants) share the same infrastructure — servers, databases, and computing resources — all owned and managed by the vendor. This shared model drives cost efficiency and also reduces administrative burden [2].
- Shared infrastructure: Just as tenants share building amenities like security, utilities, and maintenance, SaaS users benefit from shared technological infrastructure.
- Compartmentalization and security: Like individual apartments, each agency’s data is isolated to prevent access by others.
- AI-specific considerations: In some SaaS environments, AI models may leverage aggregated, anonymized data for continuous learning. Agencies should carefully review whether their data is included in these training pools [3].
On-premises servers: The private home model
On-premises solutions offer the independence of owning a home, giving agencies complete control over their infrastructure — and full responsibility for maintaining it [4].
- Complete control: Agencies can fully customize infrastructure and security, much like a homeowner designs and secures their own property.
- Full responsibility: Agencies manage all hardware, software, and physical security, requiring internal expertise.
- AI-specific considerations: Greater control over sensitive data, but also higher responsibility for cybersecurity and algorithm governance.
Government cloud: The condominium model
Government clouds operate like a condominium, where agencies control their internal units (their cloud environment), but the cloud provider (like the condo association) handles the external infrastructure, physical security, and some shared elements — while ensuring compliance with strict regulatory requirements such as FedRAMP and CJIS [6].
- Infrastructure control: Agencies retain control over their environment, while providers manage shared infrastructure.
- Regulatory compliance: Government clouds meet standards like CJIS and FedRAMP for secure law enforcement operations.
- AI-specific considerations: Secure environments facilitate AI analytics while adhering to legal and ethical constraints.
Private cloud: The townhome model
Private clouds function like townhomes, where agencies have complete control over the interior and structure of their environment (e.g., virtual machines, storage, applications) within a secure, shared infrastructure. The cloud provider (like the townhome HOA) maintains the shared external elements (physical servers, network, external infrastructure), while agencies are responsible for their own environment [7].
- Interior control and enhanced security: Agencies fully control their allocated resources, such as virtual machines, data storage, and applications within the private cloud.
- Shared maintenance: Providers manage external infrastructure while agencies control internal configurations.
- AI-specific considerations: Private clouds offer enhanced security, flexibility, and tailored environments — making them ideal for sensitive AI applications, law enforcement analytics, and model training [8]. However, storage in the cloud over time, can become costly and unsustainable.
Hybrid using a data center: The commercial co-op model
The hybrid model resembles a commercial co-op building, where agencies lease space (data center infrastructure) and maintain full control over their interior environment (equipment, configurations), while the data center operator manages utilities, physical security, and common infrastructure [9].
- Shared ownership-like arrangement: Agencies manage their specific configurations within a shared infrastructure and pay for the space they use. The data center manages all core infrastructure, pipelines, and connectivity.
- Flexible data and analytics options: Agencies can retain sensitive data on-prem, use the data center for high-performance computing for AI training, or leverage cloud analytics — optimizing cloud costs while maintaining local control.
- Centralized management: The data center handles power, cooling, security, connectivity, and uptime.
- AI-specific considerations: Combining local storage with high-performance computing optimizes AI analytics and balances cost with security.
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Recommendations for police executives
- Forecast long-term costs: Account for future scaling, vendor pricing changes and cumulative cloud costs.
- Evaluate vendor contracts: Look for pricing flexibility and exit clauses.
- Conduct security and privacy impact assessments: Focus on PII, CJIS, and AI governance across all models.
- Start small: Use pilot projects to evaluate vendors and align technology to agency missions before scaling.
Conclusion
Choosing the right AI software deployment model is a strategic decision impacting operational effectiveness, security, compliance, and long-term budgets. By understanding the trade-offs across these deployment models, police executives can make better-informed decisions that align with their missions, protect their communities, and future-proof their technology investments.
Disclaimer
Modern IT infrastructure — particularly when integrating AI applications — is inherently complex, constantly evolving and subject to ongoing regulatory changes. The housing analogies presented in this article are not intended to represent perfect, one-to-one mappings for all technology deployments. Instead, they serve as a general knowledge framework to help police executives and decision makers develop a foundational understanding of key concepts. This foundational understanding, combined with the use of security and privacy impact assessments with the agency’s IT services department or contractor, is essential for navigating the rapidly shifting technological and regulatory landscape that impacts law enforcement agencies today.
References
1. Jackson S, et al. The Impact of AI on Data Governance in Public Safety. AI and Society 36, no. 4 (2021): 913-925.
2. Deloitte Insights. Trends in Public Sector Technology Adoption, 2023.
3. Raji ID, et al. Closing the AI Accountability Gap. Proceedings of the ACM on Human-Computer Interaction 4, no. 1 (2020): 1-31.
4. Marr B. The Future of AI in Law Enforcement. Wiley, 2021.
5. Goodman B, Flaxman S. European Union Regulations on Algorithmic Decision-Making and AI. Communications of the ACM 60, no. 3 (2017): 17-19.
6. Federal Risk and Authorization Management Program (FedRAMP). Compliance Standards for Federal Agencies, 2023.
7. Cloud Security Alliance. Cloud Security for Law Enforcement, 2022.
8. LensLock, Inc. The Emerging Role of AI in Law Enforcement, 2023.
9. National Institute of Standards and Technology (NIST). Guidelines for Hybrid Cloud Implementation, 2023.
