With advanced features such as real-time monitoring, incident detection, and automated alerts, AI-based video security solutions offer powerful safety tools. However, these technologies also raise significant data privacy concerns, particularly regarding facial recognition, biometric data collection, and surveillance practices.

Privacy-first AI offers an approach to video security that prioritizes the protection of personal data while maintaining robust security standards. Creating a secure environment that respects individual privacy can be achieved by adopting a set of the following best practices:‍

1. Prioritizing Purpose: Defining the Role of AI Video Security

Before deploying an AI video security system, it is crucial to assess the organization's specific needs and the system's intended purpose. Understanding the desired outcomes, such as reducing shoplifting, enhancing physical security, or analyzing foot traffic, is essential. By clearly defining the purpose, organizations can implement a system that aligns with their security goals while incorporating privacy and security measures from the outset.

The purpose of each system directly influences the chosen technologies and privacy measures. In the retail setting, the focus is on behavior analysis to minimize customer identification. In an airport, identity verification is critical, necessitating facial recognition but with stringent safeguards. Both systems prioritize privacy by aligning their deployment with their specific security needs. Identify the role first, then move on to deployment.

2. Implement Privacy by Design Principles

Privacy by Design (PbD) is an essential framework for ensuring that privacy considerations are integrated at every stage of AI video security development and deployment. PbD requires organizations to proactively address privacy issues upfront instead of making adjustments after implementation.

For example, a retail store that uses AI-based video monitoring for theft prevention might consider minimizing the time it stores its identifiable footage. Instead of saving video records indefinitely, the store could establish policies to automatically delete footage every 24 hours as long as it contains no security incidents. Another example would be using de-identified video streams where it is possible to continue to provide insights into customer traffic without capturing personally identifiable customer data.

3. Capture Only Necessary Data 

Data minimization is a key aspect of privacy-first AI. By only collecting and processing data that’s strictly necessary for security purposes, companies can reduce the risk of data breaches and privacy violations.

For example, in a corporate office with AI video surveillance, the system could be configured to monitor general work areas rather than personal workspaces. Instead of capturing high-resolution images that could reveal individuals' sensitive information on their screens, the AI could be trained to recognize and alert only on behaviors indicative of security risks, such as unauthorized entry to restricted areas. By focusing on specific safety objectives, the system respects employees’ privacy while ensuring a secure environment for its workforce.

4. Utilize Edge AI for Local Data Processing

One way to protect privacy is to minimize data sharing with central servers. Edge AI, which processes data locally on the device, is particularly valuable for video security. By processing video data at the edge, organizations can analyze footage without uploading it to a centralized cloud server, reducing exposure to potential breaches and unauthorized access.

Consider a public transportation system using AI cameras to detect unattended bags or overcrowded platforms. Instead of streaming all footage to a central server, edge AI can analyze video data locally on each camera, alerting authorities only when it identifies an issue. This approach not only saves bandwidth and reduces latency but also limits the risk of exposing passengers’ identities.

Lumana is an example of a company whose system adheres to edge AI principles. Lumana Core stores each camera’s footage locally for highly efficient processing and an added layer of privacy protection.

5. Adopt Ethical and Transparent Facial Recognition Practices

Facial recognition is often the most controversial aspect of AI-powered video security due to concerns over misuse, misidentification, and racial bias. Companies should first consider whether facial recognition is necessary. If facial recognition is needed for optimum security, organizations should consider strict limitations on its application and ensure transparent, ethical practices.

For example, a stadium using facial recognition for ticketed entry could limit its database to registered attendees who have consented to the process and provide those who don’t wish to participate a viable alternative. By designing the system to deactivate after entry, only triggering if an individual attempts to re-enter, the organization can enhance security without overstepping privacy boundaries.

Communication and transparency are important components of ethical facial recognition practices. Informing attendees about how their data will be used and stored builds trust and keeps practices transparent.

6. Ensure Compliance with Privacy Regulations

Complying with data protection regulations, such as the General Data Protection Regulation (GDPR) in Europe or the California Consumer Privacy Act (CCPA) in the U.S., is critical for maintaining a privacy-first approach to AI video security. These laws provide specific guidelines on how data should be collected, stored, and processed, and they offer rights to individuals over their data.

For example, GDPR requires that organizations provide individuals with clear information on data collection and offer rights to access, correct, or delete personal data. When implementing AI video security, organizations should provide clear notices about data usage and incorporate mechanisms for individuals to exercise their rights.

7. Administer Strict Access Controls

System access controls are a critical component of video security privacy protection, serving as a gatekeeper for sensitive information. By restricting and managing who can view, download, or modify video footage, these controls prevent unauthorized access, mitigate insider threats, and ensure compliance with legal and regulatory privacy standards. Access controls create role-based permissions that protect individual confidentiality while maintaining organizational accountability. Through detailed access and audit logging and hierarchical security management, access controls transform video surveillance systems from potential privacy risks into responsible, ethical tools.

For example, employees of a manufacturing facility might have different tiers of access permissions to their security system based on their role. While managers might be able to view footage from cameras, only senior management or above could configure longer storage periods for footage.

8. Adopt Corporate Policies, Carry out Regular Audits and Transparency Reports

Adopting clear corporate policies and conducting regular audits are both essential for ensuring AI video security systems adhere to privacy standards. Publishing transparency reports can further demonstrate an organization's commitment to accountability and ethical surveillance practices.

A college campus using AI security cameras could conduct quarterly audits, assessing both the effectiveness and privacy impact of the system. Publishing a summary of these findings, including details on any changes or improvements made, demonstrates a commitment to privacy and allows stakeholders to remain informed about data practices.

Security and Privacy, Hand in Hand

As AI technology evolves, so do privacy protections—and companies should continuously review and update security protocols, working in partnership with legal advisors and privacy experts. Privacy-first AI video security is achievable through a combination of thoughtful design, data minimization, local processing, privacy-preserving technologies, ethical facial recognition practices, regulatory compliance, and transparency. By adopting these practices, companies can create secure environments that respect privacy, enhance public trust, and set a new standard for the responsible use of AI. The future of security lies not in invasive surveillance but in intelligent, ethical monitoring that protects both assets and personal freedoms.