Purchasing video and photo anonymization software is not a routine technology decision. It is a choice that affects data protection compliance, operational efficiency, information security, organizational reputation, and increasingly also the way artificial intelligence is used. In a world where CCTV footage, body-worn camera recordings, industrial monitoring, event videos, and AI system test data are processed at scale, a visual data anonymization tool must be not only effective, but also reconstruction-resistant, scalable, and secure.
The guide below explains which questions are worth asking a software provider before an organization decides to implement a solution for anonymizing faces, body silhouettes, and license plates.
Why Choosing an Anonymization Tool Is Strategically Important
Image anonymization is increasingly required by law when handling data access requests, sharing recordings with third parties, or publishing materials in external channels. GDPR, UK GDPR, CPRA, and supervisory authority guidance (EDPB, ICO, CNIL) clearly indicate that visual data processing must comply with the principles of data minimization, integrity, confidentiality, and accountability. A poorly selected tool may lead to insufficient privacy protection or, conversely, excessive interference that reduces the evidentiary or analytical value of the material.
That is why the purchasing decision should be made jointly by security, IT, compliance, data protection, and business process owners.
Detection Accuracy – The Foundation of Secure Anonymization
Anonymization is only as strong as its weakest undetected element. If software cannot detect faces in low light or license plates in fast motion, the risk of re-identification increases.
What detection models does the system use?
It is worth verifying that the tool uses modern deep learning models, not only traditional computer vision algorithms. Neural networks handle diverse scenes, lighting conditions, and camera angles more effectively.
How does the software perform in real-world conditions?
IR camera footage, night recordings, motion blur, masks, helmets, or partially covered faces are everyday realities in surveillance. The provider should have test results for such conditions.
Does the system detect more than just faces?
Secure anonymization should also cover body silhouettes, clothing elements, license plates, workstation screens, and other identity-related objects. Solutions such as Gallio PRO offer multi-class detection precisely to reduce context-based identification risk.
Strength and Irreversibility of Anonymization
Supervisory authorities emphasize that anonymization must be irreversible using means that can reasonably be applied. Simple, light facial blur often does not meet this criterion—especially in the era of generative models capable of reconstructing blurred images.
Has the tool been tested against AI-based reconstruction?
It is worth asking about tests using modern generative models that attempt to restore facial or plate details after blurring.
Which anonymization techniques are available?
Secure solutions offer, among others, strong Gaussian blur, pixelation, block masks, and optional replacement of faces with synthetic images. Each technique has a different privacy profile and a different impact on material usability.
Is identifying context anonymized as well?
In many cases, the background, surroundings, or distinctive clothing make it possible to identify a person. The tool should support anonymization of these elements as well when necessary.
Regulations and Data Protection Compliance
Visual data anonymization is directly linked to obligations under GDPR and other regulations. Before choosing a tool, it is worth analyzing to what extent it supports legal compliance requirements.
Is the solution designed in line with supervisory authority guidance?
EDPB guidance on anonymization and video devices, as well as national authorities (e.g., ICO, CNIL), specifies that the process must be documented, risk-assessed, and regularly reviewed.
Does the provider have experience with DSAR and the public sector?
Practical experience in handling subject access requests, police footage, or official materials demonstrates solution maturity.
Does the tool also cover metadata?
Data such as GPS, camera identifiers, and timestamps may also constitute personal or sensitive data. Comprehensive anonymization should take these into account.
Deployment Model and Integration with Existing Processes
Even the best algorithm loses value if it cannot be embedded into real workflows. Matching the solution to existing infrastructure and processes is critical.
Which deployment model is available?
In many sectors—public safety, critical infrastructure, healthcare – sending footage to the cloud is not acceptable. Therefore, local (on-premise) or network edge deployment options are essential.
Does the system support automation and batch processing?
Organizations often manage thousands of hours of recordings. They need task queues, watched folders, and APIs that make it possible to integrate anonymization into existing video management systems.
What does integration with other systems look like?
Support for popular formats and integrations with VMS, DAM, or incident systems significantly eases adoption.
Solutions such as Gallio PRO were designed specifically for integration with existing processes and infrastructure. If you want to see what this can look like in practice, it is worth downloading the free Gallio PRO demo.
Information Security and Solution Architecture
Photo and video anonymization software processes some of the most sensitive data. Therefore, security requirements must be treated as equally important as detection accuracy.
Are data encrypted at rest and in transit?
At minimum, the standard should be AES-256 encryption and TLS 1.2 or newer for transmission.
Does the provider have proof of security standards?
Certifications such as ISO/IEC 27001 or SOC 2 reduce vendor-side risk.
Are full logs and audit trails available?
The solution should record processing operations, exports, and user actions, supporting the accountability principle.
Performance and Future Scalability
The volume of video and photo data is growing year by year. A tool selected today should be capable of handling significantly higher future workloads – not only in terms of file volume, but also resolution and scene complexity.
Does the system support high resolutions and complex camera setups?
4K, fisheye lenses, and multi-camera systems are increasingly common requirements.
Does anonymization use GPU acceleration?
Deep learning models run efficiently primarily on graphics processors. Lack of acceleration often means very long processing times.
Are there limits on the number of jobs or users?
Rigid licensing limits can create bottlenecks, especially in multi-branch organizations.
Quality Control and Human-in-the-Loop Processing
Even very advanced anonymization should be verifiable. A human operator must be able to check what has been detected and anonymized, and correct potential errors.
Does the system provide detection preview?
Operators should see marked areas that will be anonymized and have the ability to correct them quickly.
Can the anonymization level be configured?
Different scenarios—from training materials to incident footage – require different blur intensity and redaction scope.
Does the tool support human-in-the-loop workflow?
In many processes, operator review and approval before final export are essential.
Vendor Partnership and Post-Deployment Support
A vendor of anonymization tools becomes part of an organization’s compliance and security strategy. What matters is not only what the product offers at purchase time, but also how it will be developed and supported over the coming years.
Are benchmarks and quantitative data available?
Without objective metrics, it is difficult to compare solutions reliably.
What is the model update policy?
Clothing styles, camera types, and attack techniques evolve. Detection and anonymization models must be updated.
Are implementation support and documentation provided?
Anonymization is a complex area – training, best practices, and responsive technical support are crucial.
When planning implementation, it is worth choosing a partner that combines legal and technological expertise. Gallio PRO offers both an advanced visual data anonymization tool and specialist support for designing a secure process.
FAQ – Buying Video Anonymization Software
Is face blurring alone enough for full anonymization?
Not always. A person can often be recognized by body shape, gait, clothing, or scene context. Secure anonymization should also account for these elements.
Are all tools resistant to AI-based reconstruction?
No. Only solutions tested against reconstruction attacks using modern generative models provide a high level of protection against restoring blurred areas.
Should anonymization be performed before exporting footage externally?
Yes. From a security and compliance perspective, raw footage should not leave the organization. Anonymization should be completed before sharing materials with third parties.
Is cloud anonymization GDPR-compliant?
It can be, provided that appropriate technical and organizational safeguards are in place, processing terms are transparent, and data storage jurisdiction is controlled. In many sectors, the safer choice remains the on-premise model.
Can anonymized recordings be used as evidence?
Yes, provided the original is securely stored and available when needed, and the anonymization process is documented. The anonymized copy can be used in external communication or reporting.
Where should anonymization tool testing begin?
A good starting point is samples from real cameras, representative of different scenarios—day, night, crowd, rapid motion. It is also worth testing performance and quality on longer recordings.
Bibliography
Oh, S. et al., “Facial deblurring using deep generative networks”, CVPR 2018.
EDPB, “Guidelines 05/2021 on anonymisation techniques”.
Information Commissioner’s Office (ICO), “CCTV and video surveillance guidance”.
ISO/IEC 27001, Information Security Management Standard.
