Like so many operational functions of commercial real estate today, artificial intelligence is gaining a strong foothold in security and surveillance. Vendors like Deep Sentinel and Cloudastructure are scaling quickly, offering AI-powered camera systems that analyze video feeds in real time, detect threats, and escalate alerts to security personnel.
Deep Sentinel’s “intervention-first” AI model claims to not only detect motion but also evaluate behavior, context, and threat level. “We intervene in under 30 seconds,” CEO David Selinger recently said. “Speed is the difference between deterring a crime and recording one.” That’s a bold claim, and something that in the not-too-distant past may have seemed more fitting for science fiction than real life.
Yet these tech capabilities are increasingly available today. And while they are used across different CRE asset classes, their impact may be most visible in multifamily. What was once a low-tech operational function with analog cameras and DVRs has become a more strategic investment tied to risk management, resident experience, and operational credibility.
This Insights by Blueprint report provides a critical examination of AI-enhanced security and surveillance in multifamily housing.
Key takeaways include:
- What AI-enhanced surveillance really is, its recent advancements in the past five years, and what its benefits are for multifamily operators
- How multifamily operators can measure ROI, one of the trickier parts of deploying these systems
- How to navigate compliance and privacy risks
- The four pillars of smart AI surveillance deployment
What is AI-enhanced surveillance, exactly?
Before we delve into more actionable insights, let’s define what AI-enhanced security and surveillance in multifamily actually entails and what it promises.
Early applications of AI in video surveillance were rudimentary, according to Patrick Barry, CEO of BluBØX, a Massachusetts-based firm that designs and manufactures cloud-based physical security systems. Video analytics algorithms detected motion and distinguished objects based on size and movement, such as differentiating between a person and a car. However, recognition accuracy was limited, and object identification was basic at best.
With the rise of neural networks, object recognition has undergone significant improvements. AI can now accurately identify a wide range of objects, including people, cars, buses, trucks, stop signs, and other similar items. This shift over the last five years has made object recognition a baseline expectation for AI in surveillance systems.
Beyond recognition, tracking has emerged as another core capability. AI can now follow objects frame by frame, allowing systems to determine direction, speed, patterns of movement, loitering, and points of origin or destination.
More recently, segmentation and scene understanding have been layered onto these capabilities. Visual language models, combined with large language models, enable systems to parse a scene, identify core components (such as doors, walls, floors, windows, desks, and people), and describe the environment in natural language. This ability to contextualize both background and moving objects is relatively new, having been developed only about two years ago, according to Barry. It represents a significant leap forward.
Cost effectiveness
Patrick Barry of BluBØX likens AI-enhanced cameras to having 24/7 security officers.
For comparison, Barry notes:
- Traditional security officers cost roughly $75,000 annually.
- AI-powered cameras cost around $3,000 each and monitor threats in real time, often much better than human security guard, in Barry’s estimate, without the need for breaks and without getting tired.
AI can not only observe but also summarize activity: hourly, over an eight-hour shift, or across a full day. It identifies risks, highlights anomalies, and produces narrative summaries of what occurred. This type of continuous risk assessment and contextual reporting is beyond the ability of human security officers working alone.
Industry momentum
Opinions vary on how quickly AI security is gaining adoption.
Of the 90 million cameras installed globally, only a fraction use even basic analytics, and fewer still deploy what could be considered “true AI,” according to the Security Industry Association’s 2025 Megatrends report. Use of these systems is most pronounced among large multifamily operators, new developments, and properties where crime is a significant concern.
Cloudastructure, a cloud-based AI video surveillance and remote guarding solutions firm, has grown a steady list of clients in the multifamily sector. Whitney Fraser, Cloudastructure’s VP of Sales, said that security concerns in rental housing have become a significant concern since the pandemic, when worries over increased crime began to rise.
Fraser says Cloudastructure works with 11 of the top 20 U.S. multifamily companies, including Avenue5 Residential and Fairfield Residential. The use of advanced security and surveillance technology in multifamily housing mirrors the practices of local law enforcement agencies, which are increasingly utilizing license plate readers, biometrics, and interconnected camera networks. So, it makes sense that multifamily has become an additional growth sector for the security industry.
“We’ve had massive interest from multifamily owners,” Fraser said. “It doesn’t matter if it’s Greystar or a mom-and-pop landlord. I’d say that maybe two years ago we were in the early adoption phase of these technologies in multifamily, but we’re much further along now.”
Like Patrick Barry of BluBØX, Fraser emphasizes that the primary benefit of AI-enhanced security is its ability to transition security from reactive to proactive. “I come from a property management background, and security has always been very reactive,” she says. “These new technologies have the potential to change multifamily security tremendously.”
Trust, transparency, and targeting
Across multifamily communities, operators say that most residents welcome increased surveillance upgrades. Package theft, car break-ins, and vandalism can be common frustrations, and visible security upgrades increase perceived safety and even marketability.
It often doesn’t matter if crime rates in an area or community have increased or not. The perception of safety usually matters more, and numerous surveys show that multifamily residents place a high value on security, especially in Class A communities.
However, support may erode when transparency is lacking. Residents who feel blindsided by hidden monitoring or vague policies are more likely to push back, creating reputational risk for owners. Operators must treat transparency as a core part of the security rollout. Disclosure is as much a trust-building exercise as a compliance priority.
Effective practices include:
- Lease addenda that outline surveillance coverage and data use.
- Posted signage at all monitored locations to meet legal requirements and reduce liability.
- Resident FAQs and onboarding materials that explain what is monitored, how footage is used, and how long data is retained.
- Community forums or Q&A sessions when new technologies are rolled out, allowing residents to voice their concerns.
Clear communication can transform surveillance from a potential flashpoint into a leasing advantage, particularly for younger renters who expect “smart security” features but are sensitive to privacy concerns.
Can you really measure deterrence?
Unlike energy retrofits or amenity upgrades, multifamily operators say the ROI of security upgrades is more challenging to measure. Vendors often promise reduced theft and higher resident satisfaction, but it is harder to demonstrate measurable crime reduction.
Cloudastructure has tried to do this by often pointing to its real-time crime deterrence capabilities. The company has repeatedly reported that its system discourages unwanted activity more than 98% of the time. It presents this figure as evidence that the platform helps stop theft, trespassing, and other risks before they turn into criminal incidents.
“During the first five months of 2025, the company’s AI system generated 3.28 million alerts, including 929,019 in May alone,” Cloudastructure says in a June 2025 press release. “The platform also enabled 13,129 live audio interventions, allowing trained remote guards to de-escalate incidents in real time and prevent situations from escalating into criminal acts.”
The press release continues, “The incidents Cloudastructure helped deter span a wide spectrum of security concerns. These include common issues such as suspicious behavior, trespassing, theft attempts, and illegal parking, as well as more serious situations like threatening conduct, encampments in shared spaces, and physical confrontations.”
The deterrence rate is derived from internal company metrics, which compare the number of incidents flagged and de-escalated against the total volume of threatening activity detected. While these statistics appear impressive, it’s unclear if they move the needle much in determining ROI for multifamily operations.
It also raises the question of the quality of the real time alerts. For example:
- With so many alerts issued, how many of those were false alarms?
- And does the frequency of alerts ever cause “alarm fatigue” for the human security guards who are supposed to be monitoring them?
These are questions that operators may have to ask. While a 98% deterrence rate sounds incredible, it also begs the question of how Cloudastructure calculates its internal metrics to arrive at such a spectacular data point.
Multifamily operators say deterrence is indeed a tangible benefit. But crime data is something property managers may be far less interested in tracking than police departments are. Operators will likely find more value in the ease of collaboration with local police departments and the operational reliability of cloud-based platforms, compared to legacy DVRs and analog systems.
Other than the deterrence rate, Cloudastructure meticulously tracks various data for the multifamily communities it serves, sending clients monthly reports with key metrics. Whitney Fraser, VP of Sales at Cloudastructure, said one area that helps build an ROI case is labor savings on security personnel. “One of our clients is saving about $100,000 annually on labor costs,” Fraser claims.
Fraser adds that advanced security tech may also solve common, irritating problems for operators. She provided examples of leveraging surveillance tech to catch residents bringing in unauthorized pets as well as using it to build a case for evicting a problem tenant.
To build a credible ROI case, owners may track a broader set of operational and experiential metrics beyond raw incident counts:
- Incident response time: The time it takes for on-site staff or remote teams to respond when an alert is triggered.
- Police requests for footage: A proxy for how valuable and reliable the system is in resolving disputes. Fraser says Cloudastructure streamlines this process, as well. Footage in its system can be easily searched and retrieved, similar to a Google search, and then emailed to police or legal counsel.
- Package theft resolution rates: An issue that residents notice immediately and one that impacts tenant satisfaction.
- Guard staff reallocation savings: Quantify reductions in guard hours at large sites once AI-assisted monitoring is in place.
- System uptime and reliability: Demonstrating resilience compared to older DVRs that frequently fail or overwrite footage.
- Insurance impacts: Yi Jin of AlphaVision, another firm that provides AI-driven security and surveillance solutions, notes that insurers may offer property insurance discounts of up to 10% to multifamily owners who utilize the systems.
Framing ROI as a blend of risk reduction, liability protection, and resident experience improvement is often more persuasive to ownership than attempting to calculate dollar-for-dollar crime savings.
Minimizing wasted spend
The steep upfront expense makes it impractical for most operators to introduce AI-enhanced surveillance and security systems across every property at once. A better approach is to phase in deployments selectively, guided by detailed criteria. This reduces excessive costs and builds trust with investors, residents, and community partners. Mistakes such as heavy spending on low-crime properties weaken credibility and slow adoption across the portfolio.
One way to guide decision-making is to use a structured matrix that weighs cost, risk, and return:
- Target higher-risk sites: Start with properties where crime issues are above average, since the deterrent effect and ROI will be most apparent.
- Coordinate with law enforcement: When collaboration is a priority, prioritize tools that can integrate directly with police systems, such as license plate recognition.
- Differentiate by property type: It’s generally easier and cheaper to add these systems into new developments, while existing properties may require a phased retrofit plan.
- Balance cost against value: Set a maximum spend per site so that security upgrades remain proportional and don’t diminish the overall impact.
The most effective operators roll out in waves, using early pilots to validate claims, generate internal case studies, and refine deployment playbooks before scaling across their portfolio.
Smart security on a budget
The surveillance tech stack in commercial real estate is undergoing a significant shift. Legacy on-premises systems built on DVRs and hardwired cameras are increasingly being displaced by cloud-based platforms. These solutions promise to improve reliability and accessibility, but also reduce the IT burden of maintaining on-site servers that frequently fail or overwrite footage.
Bring Your Own Camera (BYOC) approaches are helping speed up adoption.
With this approach, operators don’t have to rip out their existing cameras to upgrade. Many of these platforms are built to work with the hardware that’s already in place, layering AI on top of what’s there. That saves money at the start, keeps good equipment from being tossed, and gives mid-sized operators a way to move into smarter surveillance without committing to an expensive, full replacement.
When evaluating vendors, operators should favor solutions that balance innovation with flexibility:
- Cloud-first platforms with remote access: Systems should be accessible anytime, anywhere, without the operational headaches of on-premises storage.
- AI features that can be toggled on/off: Owners need control over how and when AI capabilities, such as facial recognition or behavioral analytics, are deployed, especially given the varying state-level privacy laws.
- Open integrations: Look for platforms that seamlessly connect with access control systems, resident-facing apps, and local law enforcement databases. Avoid walled-garden systems that trap owners in a single vendor ecosystem.
Blueprint recommendations:
- Shortlist BYOC-friendly providers: Prioritize vendors that allow existing cameras to be folded into AI-driven monitoring, stretching budgets further.
- Avoid vendor lock-in: Confirm interoperability before signing multi-year contracts. Ensure the system can integrate with multiple hardware brands, access control systems, and law enforcement feeds.
- Test vendor claims: Launch limited pilots at 1–2 properties to validate ROI, AI accuracy, and reliability. Track false positives, staff adoption, and law enforcement feedback before scaling.
- Future-proof procurement: Structure contracts to allow for upgrades, adding new AI analytics or integrations over time without full system replacement.
Navigating compliance & risk
The rules governing AI surveillance remain patchy and complicated.
States such as California, Illinois, and Colorado have introduced tougher standards on biometric data, consent, and record-keeping, while many other regions still have minimal guidance or none at all. This uneven landscape leaves a significant gap. In areas with fewer restrictions, owners might be tempted to test new systems more freely, but in doing so, they take on a higher risk if disputes, lawsuits, or data breaches arise.
Federal regulation is also lagging, leaving operators to navigate a patchwork of state privacy laws, municipal ordinances, and contractual obligations. The result is that many operators don’t fully realize how exposed they are until something goes wrong.
To reduce exposure, owners should embed privacy and compliance safeguards into their surveillance strategy:
- Conduct a Privacy Impact Assessment (PIA) before rollout, mapping the types of data collected, the duration of storage, and the individuals with access.
- Require vendor compliance attestations to shift some liability back onto providers.
- Add contract language on data stewardship to clearly define who owns the data, how breaches are handled, and who bears financial and reputational risk.
- Implement disclosure protocols proactively, even in unregulated states, to build trust and preempt future regulatory tightening.
Keep these risks in mind as well:
- Don’t bluff: In several states, it’s illegal to post signage or tell residents a property is under surveillance if cameras aren’t actually functioning. “Security theater” may appear to be effective in the short term, but it can expose owners to fines and lawsuits.
- Bias risk: AI models aren’t always neutral. Some studies, including those conducted by researchers at MIT and Penn State University, have shown inconsistent and sometimes racially discriminatory outcomes in facial recognition and behavior detection. Owners should push vendors on how models are trained, tested, and audited for fairness. While an end user, like a multifamily operator, can’t possibly control AI model bias, they should at least be aware of it when researching vendors and before selecting one.
- Don’t overdo it: Over-deployment may alienate residents and damage brand reputation. One operator told us that a Class C multifamily owner blanketed a property with over 200 cameras, which is both surveillance overkill and a waste of resources. Without careful disclosure, excessive surveillance like this is seen as intrusive rather than protective. Balance coverage with clear communication about purpose and limits.
Four pillars of smart AI surveillance deployment
Rolling out AI surveillance goes beyond simply choosing the newest tech on the market. It calls for a clear, disciplined plan. Owners who adopt systems without setting goals, budget limits, and performance measures run the risk of spending heavily on tools that appear sophisticated but offer little real benefit. A structured strategy ensures investments match business priorities, grow at the right pace, and adjust as results come in.
Define objectives
Before investing in surveillance, owners must be clear about the problem they are trying to solve. Figure out if the priority is:
- Deterrence: reducing theft, vandalism, and unauthorized access.
- Operational efficiency: replacing or reallocating 24/7 guard staff with AI-enabled monitoring.
- Resident experience: marketing “smart security” as a differentiating amenity.
Although you may be pursuing all of the above, it is essential to rank and document your objectives. A lack of clarity often leads to over-deployment or mismatched technology, such as buying license plate readers for sites where package theft is the core issue.
Budget & scope
Surveillance deployments require upfront planning around the financial model and scale:
- Capex vs. Opex: On-premises systems require heavy Capex (servers, storage, hardware), while cloud platforms spread costs over Opex subscriptions.
- Scope: Portfolio-wide deployments are rare; most operators start with pilot sites or high-crime properties.
- Lifecycle costs: Consider maintenance, upgrades, and staff training. Cutting corners here leads to unusable footage or systems that quickly become obsolete.
Owners should calculate cost per unit or per door as a benchmark for spend, creating a standard for when surveillance investments are financially justified.
Measure & adjust
The effectiveness of AI surveillance should be treated as an ongoing experiment, not a set-and-forget deployment:
- Track outcomes quarterly: Incident response time, police requests for footage, package theft resolution rates, and guard cost savings.
- Resident feedback: Gauge whether perceptions of safety improve or if surveillance feels intrusive.
- Performance review: Use data to decide whether to expand, replace, or downgrade systems.
Regular measurement prevents “security theater,” which is expensive and looks impressive but delivers little tangible benefit.
Focus on holistic solutions
To maximize the value of AI surveillance, multifamily owners and operators should focus on holistic, future-ready strategies rather than isolated solutions:
- Move beyond one-dimensional tools: Cameras alone are not sufficient. The most effective security strategies integrate multiple systems to create a comprehensive view of property activity.
- Adopt integrated platforms: Unified solutions that combine cameras, access control, alarms, elevators, and visitor management under AI oversight provide greater visibility and reduce security gaps.
- Focus on scalability and the cloud. Cloud-based platforms strengthen both security and resilience while making it easier to expand across an entire portfolio. Because software is updated continuously, operators gain access to the newest AI features without having to invest in expensive system replacements.
Unified solutions deliver a cohesive security ecosystem that enhances every area of the property, eliminating the inefficiencies and blind spots created by siloed systems.
Final takeaways
Artificial intelligence in surveillance is part of a broader shift in multifamily property management. The same methods are being applied across various building systems, including HVAC, elevators, water, and access control, where AI can analyze sensor data, generate summaries, and provide a real time, building-wide view of operations.
This marks a fundamental shift from fragmented monitoring to unified intelligence that detects, contextualizes, and provides actionable advice. Success will depend on treating AI not as a gadget but as part of an integrated strategy, rolled out with discipline, transparency, and a focus on ROI through risk reduction, liability protection, and resident satisfaction.
While AI-enhanced security and surveillance may be the way of the future, it’s essential to note that most systems will still require a “human-in-the-loop” approach. Multifamily operators may be able to reduce guard labor, but guards will need to be retrained to use these systems and respond appropriately to the promised real-time alerts and other capabilities.
Multifamily owners who thrive with these technologies won’t be the ones chasing the flashiest gadgets. The real advantage will belong to those who weave AI into everyday operations, turning “smart security” from a marketing slogan into a practical, long-term advantage.
-Nick Pipitone
