Compliance
Flock’s regulatory compliance efforts encompass a broad range of activities aimed at adhering to federal and state laws and regulations, as well as industry standards. This includes complying with requirements from agencies like the FBI’s policies for Criminal Justice Systems (CJIS), the US Data Privacy Framework, and the NIST Cybersecurity Framework
Privacy by Design
Privacy by Design requires a proactive, ethical, and integrated approach for the deployment of principles in product architecture and technologies to enable privacy rights into Flock’s solutions from the start. Principles including data minimization, limitations on retention, and least privileged access facilitates privacy for customers and communities.
Responsible Use of AI
We recognize that the power of Machine Learning in cybersecurity comes with a profound responsibility. We believe that ethical AI is fundamental to building trust and ensuring our technology serves its intended purpose of enhancing safety and efficiency without compromise.For us, AI Ethics means rigorously upholding principles such as:
Fairness: We are dedicated to building ML models that perform equitably across diverse real-world conditions, minimizing unintended outcomes that could arise from training data or environmental factors. Our processes include continuous monitoring and validation to ensure accuracy and impartiality in our classifications and readings.
Transparency: We strive to understand how our ML models arrive at their predictions. This commitment to explainability ensures that our technology can be auditable, its decisions understood, and its performance continuously improved.
Accountability: We take full ownership of the outcomes generated by our AI systems. Our internal governance structures and expert teams ensure that our models are developed, tested, and maintained responsibly.
Reliability: Our dedication to quality extends to the resilience of our ML models. Through rigorous testing, continuous validation, and a focus on adversarial robustness, we ensure our systems deliver consistent and dependable results in challenging environments.
Data Privacy and Security: Recognizing that ML relies on vast datasets, we implement stringent data protection measures throughout the entire data lifecycle. This includes secure data handling, access controls, and adherence to privacy regulations, ensuring the data used to train and operate our models is always protected.
Data Retention
We understand that effective data retention is not merely a regulatory checkbox; it's a proactive strategy to ensure the integrity of our systems, and preserve both the confidentiality and availability of your information. Flock’s retention policy requires retaining customer data for a default period of 30 days and also provides flexibility where our customers demand different retention periods.
Data Elements Chart
Data Flock Collects | Description |
|---|---|
ALPR & Vehicle Intelligence Data | |
License Plate Number | The alphanumeric characters extracted from the license plate |
License Plate Image | The image frame containing the detected license plate |
Vehicle Image | A broader image of the vehicle, providing context including model and color |
Time Stamp | Date and time of the license plate capture |
Additional Vehicle Attributes | Make, color, type, plate type and state, any notable markers |
iOS Platform & Application Usage Data | |
User Identifiers | Unique ID’s assigned to customers’ users within the platform |
Use Access Logs | |
User Identifiers | Unique ID’s assigned to customers’ users within the platform |
Performance Data | Application performance metrics |
Operational & Account Management Data | |
Customer Account Information | -Names, e-mail addresses, phone numbers of primary contacts -Billing Information -Contract Details |
Support & Communication Data | Records of customer service interactions, support tickets, emails. |
Contractual Data | Details of service agreements, SLAs, legal terms |
Audit & Compliance Data | Logs related to user access to the platform, system changes and security incidents. |
Machine Learning Training & Improvement Data | |
Annotated Image Data | License plate images, vehicle images, with human-verified bounding box annotations and labels |
Model Performance Metrics | Data on model accuracy, precision, recall, false positive/negative rates, especially broken down by challenging conditions such as low light, angle, blur. |
Customer Feedback on Performance | Aggregated and analyzed data from customer-flagged incorrect reads or classifications. |