Every day your platform operates without proactive CSAM detection is a day you're gambling with catastrophic business risk. As regulatory bodies tighten their grip and public scrutiny intensifies, platforms relying solely on user reports to identify Child Sexual Abuse Material are walking a dangerous tightrope.
The consequences? Devastating fines under the UK Online Safety Act, irreparable brand damage, and the very real possibility of platform shutdown. While major tech companies have fortified their defenses with sophisticated detection systems, smaller platforms have become prime targets for bad actors seeking to exploit weak content moderation, potentially transforming your digital space into a liability overnight.
This comprehensive guide empowers platform operators to move beyond reactive moderation and implement robust CSAM detection systems that protect both their users and their business interests. Through our structured approach, you'll discover:
A comprehensive risk assessment framework to identify vulnerabilities across your platform. This assessment helps you pinpoint areas that could trigger non-compliance penalties or create legal exposure.
Practical, resource-conscious strategies tailored for smaller platforms guide you through implementing automated detection systems
The result? A robust defense strategy that ensures regulatory compliance while protecting your platform's reputation and long-term viability.
Risk assessment: Understanding your platform's CSAM vulnerabilities
Before implementing any CSAM detection measures, it's crucial to conduct a thorough risk assessment. This process helps you to
Map potential vectors for CSAM distribution specific to your platform
Design targeted prevention strategies based on actual risks
Allocate resources efficiently to high-risk areas
Our risk assessment will focus on four key factors:
Content sharing capabilities: How users interact with and share content on your platform.
User interaction features: The mechanisms that facilitate communication between users.
User verification and authentication: Measures in place to verify user identities and ages.
Platform-specific moderation challenges: Factors that influence the difficulty of content moderation.
We will categorize the risk level for each factor as follows:
Low risk: Minimal risk of CSAM circulation.
Medium risk: Moderate risk requiring proactive mitigation strategies.
High risk: Significant risk requiring immediate and robust countermeasures
Step-by-step process to conduct your risk assessment
Follow this structured approach to assess your platform's risk level. For each feature category, evaluate your current capabilities and identify gaps in protection.
Category 1: Content sharing capabilities
File uploads and sharing
Examine all areas where users can upload or share files:
Profile pictures/avatars
Media galleries
File attachments in messages
Document sharing features
Cloud storage capabilities
| Risk level | Reason |
|---|---|
Low risk
| Text-only platform with no file-sharing capabilities. Since users cannot upload files, the risk of CSAM is significantly reduced.
|
Medium risk
| Platforms that allow file sharing with moderation systems in place (e.g., file type restrictions, automated scanning, and size limits). While moderation reduces risk, the absence of AI/ML detection means some harmful content may still bypass safeguards.
|
High risk
| Platforms with unrestricted file sharing and no moderation controls. Such platforms are highly vulnerable to exploitation by bad actors for sharing harmful content.
|
Link sharing
Assess how links can be shared across your platform:
Chat messages
Posts/comments
Profile sections
Embedded content
| Risk Levels | Reason |
|---|---|
Low risk
| No link-sharing capabilities. Users cannot share external links, eliminating the risk of sharing links that host CSAM.
|
Medium risk
| Link sharing allowed with scanning and verification (e.g., automated checks for known CSAM URLs).
|
High risk
| Unrestricted link sharing. Bad actors can share CSAM via external hosting links.
|
Category 2: User interaction features
Direct messaging
Evaluate all private communication channels, regardless of your industry or target audience:
One-to-one messaging
File sharing in DMs
Message request systems
| Risk Level | Reason |
|---|---|
Low risk
| Platforms without messaging features. With no capability for users to exchange private messages, the risk of CSAM sharing through messaging is effectively eliminated.
|
Medium risk
| Messaging features with robust keyword filters and AI/ML-based moderation systems. These tools significantly reduce the risk by detecting and flagging harmful content. However, they may not catch nuanced or novel CSAM that falls outside predefined filters or detection models.
|
High risk
| Messaging features with no moderation or basic keyword-only filtering. This creates a high risk, as bad actors can exploit the lack of oversight to share CSAM in private conversations undetected.
|
Group chats and channels
Assess group communication features:
Group size limits
Content sharing permissions
Moderation capabilities
Private vs public groups
| Risk levels | Reason |
|---|---|
Low risk
| Platforms without group features. Without the ability to create or join groups, there is no risk of CSAM sharing in group settings.
|
Medium risk
| Group features with robust moderation, including content filters and automated detection systems. These measures help mitigate the risk, but their effectiveness depends on the sophistication of the moderation tools and policies.
|
High risk
| Group features with no moderation or minimal oversight. This creates a high risk, as groups are frequently exploited by bad actors to share harmful content, including CSAM.
|
Comments and reactions
Review commenting capabilities
Text comments
Media attachments
GIFs/stickers
Reply threads
| Risk levels | Risk profile |
|---|---|
Low risk
| Comments are not allowed. This eliminates the risk of CSAM being shared through the comment section.
|
Medium risk
| Comments are allowed but limited to text only. While this reduces the risk of direct media sharing, CSAM can still be shared indirectly through coded language or text links.
|
High risk
| Unrestricted media sharing in comments with no moderation. Allowing multimedia such as images, videos, or stickers creates a high risk, as bad actors can directly share CSAM in the comment section.
|
Live streaming and calls
These are particularly high-risk features that require special attention. They can be used for sharing real-time abuse or unknown CSAM without the ability to record or store content for audit purposes or hashing. This makes moderation and oversight more challenging.
One-to-one video calls
High risk, as private, unmoderated calls can be used to share real-time CSAM or engage in harmful activities.
Group video calls
Even higher risk, as multiple users can engage in real-time CSAM sharing or abuse within an unmonitored group setting.
Live streaming to audiences
Particularly vulnerable to the live sharing of CSAM or abuse, with limited oversight of the broadcast.
Risk levels:
| Risk levels | Reason |
|---|---|
Low risk
| No live streaming or calling features. Without the ability to engage in real-time streaming or calls, there is no risk of sharing CSAM or engaging in live abuse.
|
High risk
| Any live streaming/calling capabilities due to:
Potential for streaming real-time abuse
Challenges in moderating live content
Resource-intensive monitoring requirements
Difficulty in preventing screen recording
|
Stories and temporary content
Assess ephemeral content features
Disappearing messages
Story features
Temporary media sharing
Archive capabilities
| Risk Levels | Risk Profile |
|---|---|
Low risk
| No temporary content features
|
Medium risk
| Moderate risk if the content disappears quickly but some checks are in place.
|
Category 3. User verification
Evaluate your verification workflow to identify potential vulnerabilities. Use the table below to assess the effectiveness and risk levels of different verification methods. This will help you understand how each approach impacts account security and the likelihood of bad actors exploiting your platform
| Verification method | Risk level | Comments |
|---|---|---|
Anonymity not allowed; strong ID verification
| Low risk
| Reduces the likelihood of bad actors creating accounts. Requiring IDs linked to a person deters CSAM sharing, as users are aware they can be easily identified and reported to law enforcement.
|
Basic verification (e.g., email)
| Medium risk
| Users can still use fake or temporary email accounts, making it harder to trace them. This increases the chances of CSAM sharing, as bad actors may perceive less risk of being caught.
|
No user verification
| High risk
| The absence of any verification allows full anonymity, making it extremely easy for bad actors to create accounts and share CSAM without fear of accountability.
|
Category 4: Platform-specific moderation challenges
Factors such as content volume, distribution speed, and technical resources significantly affect the platform's vulnerability to CSAM sharing. The table below assesses these factors and their influence on the risk level of your platform being used for CSAM activities:
| Factor | Low Risk | Medium Risk | High Risk |
|---|---|---|---|
Content volume
| Small amount of UGC. Makes CSAM easier to detect and prevent.
| Moderate UGC. Some CSAM might evade detection due to system limitations.
| High UGC volume requires proactive detection; without it, the risk of CSAM proliferation increases.
|
Distribution speed
| Delayed posting with need for approval and content filtering allows time for manual or automated CSAM reviews.
| Review before posting helps but could miss CSAM if moderation resources are stretched thin.
| Instant sharing poses a significant risk as CSAM can spread before detection or removal.
|
Moderation resources
| A dedicated team powered with CSAM detection tools and automated content moderation system ensures quicker detection and reporting of CSAM.
| Limited teams may delay action on CSAM content, increasing the risk of harm.
| No dedicated moderation makes it nearly impossible to address CSAM effectively.
|
Content types
| Text only platforms have minimal risk of CSAM sharing.
| Limited media types reduce but don’t eliminate the risk of CSAM.
| Platforms supporting all media types (images, videos, live streams) face the highest CSAM risk.
|
User base size
| Small, verified user bases discourage CSAM sharing as users know they can be identified.
| Large, partially verified user bases pose a moderate risk as anonymity is not entirely eliminated.
| Very large, unverified user bases make it easy for bad actors to share CSAM anonymously.
|
If your platform falls within the high or medium risk categories, it's essential to take immediate action to protect your users and comply with legal requirements. In the next section, we’ll explore strategies and tools that can help you effectively identify and prevent CSAM sharing
CSAM detection and prevention strategies
Use hash matching to detect known CSAM images and videos
Hash matching is one of the most effective and proactive defenses against the sharing of CSAM on digital platforms. It works by comparing the unique digital fingerprint (hash) of content uploaded to your platform against a database of known CSAM hashes maintained by trusted organizations.
For platforms that allow user-generated content (UGC), particularly in formats like images and videos, integrating hash matching into your content upload process is critical. If your platform scores high risk in the content sharing category during the risk assessment, you must mandatorily set up a hash matching system. This system will screen each piece of content before it’s uploaded to the platform, ensuring that any known CSAM is automatically identified and removed.
Best for
Platforms enabling media sharing, especially images and videos.
Platforms with high content upload volumes where manual review is impractical.
Use cases requiring rapid and automated flagging of known harmful content.
How does hash matching work in practice?
Step 1: Hashing uploaded content
Once a user uploads content to your platform, the file is hashed using cryptographic or perceptual algorithms (e.g., MD5, SHA-1, or perceptual hashing for images and videos),
Step 2: Comparison against CSAM Hash lists
The generated hash is then checked against databases of known CSAM hashes maintained by global organizations. If the hash matches an entry in the database, the content is flagged as CSAM.
Several organizations around the world maintain databases of known CSAM hashes. Some of the most notable organizations providing these hash lists include:
National Center for Missing & Exploited Children (NCMEC): A key organization in the fight against CSAM, NCMEC maintains a hash database that contains verified CSAM from around the world.
Internet Watch Foundation (IWF): An international organization that works to remove child sexual abuse content from the internet, IWF also maintains a hash database of known CSAM.
Google and Tech Coalition: Major tech companies and coalitions have created and shared hash databases that can be integrated with content moderation systems to help prevent CSAM distribution.
Step 3: Action on Match
The flagged content is then handled according to the platform’s moderation policies, which may include removal, user account suspension, and reporting to authorities.
Notable hash matching tools
Integrating the right hash matching tools is a key step in combating CSAM on your platform. These tools help detect known CSAM materials efficiently by leveraging extensive hash databases and advanced AI technologies. Below are some widely used tools, along with their unique features and how to get started with each.
1. PhotoDNA Cloud Service (Microsoft)
PhotoDNA is the industry standard for detecting image-based CSAM. It uses robust perceptual hashing technology to identify known harmful content, even when the material has been altered. Trusted by major tech companies, PhotoDNA is widely regarded for its high accuracy and minimal false positives.
Best for
Platforms needing a highly accurate and trusted image-based detection system.
Key features
Detects altered versions of known CSAM, such as resized or cropped images.
Free for all qualified organizations.
Focused on image content detection
Approved organizations connect through a REST API, with API keys issued post-vetting.
To use the PhotoDNA Cloud Service, visit the application page.
2. Google’s CSAI Match (Child Safety Toolkit)
Google’s Child Safety Toolkit includes two core APIs: the Content Safety API and CSAI Match. While the Content Safety API focuses on identifying and classifying previously unseen images and videos, CSAI Match specializes in detecting abusive video segments by matching them against Google’s database of known abusive content.
Best for
Platforms dealing with video content sharing.
Key features
Designed specifically for matching known abusive video segments.
Provides fingerprinting software and APIs for seamless integration.
Identifies portions of videos matching known CSAI and categorizes the content type.
Regular database updates from trusted sources ensure accurate detection.
To use Google’s CSAI Match, submit an interest form via the toolkit application page.
3. Safer by Thorn
Safer by Thorn is a comprehensive CSAM detection suite built specifically for organizations, offering an end-to-end solution designed with trust and safety teams in mind. Unlike PhotoDNA or CSAI Match, Safer combines advanced hash matching with AI-driven predictive capabilities to address both known and emerging threats.
Best for
Organizations seeking a SaaS solution that integrates seamlessly with trust and safety operations.
Platforms requiring both hash matching and predictive AI for proactive moderation
Key features
Matches uploaded content against the largest known CSAM hash database, aggregating 57.3 million hashes from trusted global sources.
Uses state-of-the-art AI to proactively detect new CSAM.
Flags potentially harmful conversations and interactions that indicate child exploitation.
Setting up a hash matching workflow
To effectively integrate hash matching into your platform's content moderation process, follow this structured workflow:
1. Automatic content screening at upload
Begin by screening all content uploaded to your platform, whether it’s shared publicly or sent through private messages. Each piece of content must go through a hash matching verification process to ensure no CSAM is present.
If you're using PhotoDNA or Google CSAI Match, integrate their APIs directly into your content upload endpoints. These services manage the technical complexity of hash generation and database comparison internally. Your system simply needs to make API calls when content is uploaded, and the services will return match results. The PhotoDNA Cloud API documentation provides implementation examples and best practices for setting up these API calls efficiently.
If you’re using Safer by Thorn, the implementation process is more straightforward. Safer provides an end-to-end solution where the screening workflow, including hash generation and comparison, is handled automatically through their system. Their built-in dashboards allow you to monitor the screening process without additional development work.
2. Workflows to handle matched content
Your system must respond immediately and appropriately when hash matching identifies potential CSAM. This response varies based on your platform's content sharing methods:
For messaging platforms:
Implement pre-delivery screening where messages are analyzed before reaching recipients
Configure automatic message blocking when matches are detected
Store relevant metadata for reporting while ensuring proper content handling
For social platforms:
Create automated removal protocols for matched content
Implement secure quarantine systems for flagged content
Maintain detailed logs of all automated actions taken
In order to set up these actions, your hash matching results need to feed into a broader content moderation system. You have two main approaches:
Build your own system: If using PhotoDNA or Google CSAI Match, you’ll need to build custom dashboards and workflows to manage flagged content. This includes creating review queues, alert systems, and moderation team management interfaces.
Use integrated solutions: Some platforms, like CometChat, offer built-in CSAM detection filters that can be configured to automatically screen content and take predetermined actions. These integrated solutions usually come with pre-built workflows to handle detected content, as well as reporting to authorities, making the process more efficient and easier to manage.
3. Protocols for reporting to authorities
Once CSAM is detected, platforms are legally required to report it to the relevant authorities. It’s critical that your platform includes a robust, automated mechanism for secure and compliant reporting.
For manual reporting: Ensure that moderators are trained on how to properly document and submit reports to law enforcement and organizations like NCMEC via their CyberTipline. You can access the CyberTipline at report.cybertip.org. This manual process should be quick and secure, ensuring the integrity of evidence.
For Automated Reporting: Integrate NCMEC’s reporting API into your platform. This API allows your system to automatically submit reports to authorities whenever CSAM is detected, reducing delays and human error in the reporting process.
If you’re using Safer by Thorn, the tool already has built-in integrations with law enforcement agencies in the U.S. and Canada. Safer automatically connects with reporting bodies, allowing your team to file reports effortlessly. The system also provides secure storage for preserving reported content and offers a form-based UI to collect all necessary data, ensuring the reporting process is both streamlined and legally compliant.
Preventing unknown CSAM: Going beyond hash matching
While hash matching is highly effective for detecting known CSAM, it does not address the issue of unknown or novel CSAM—newly created or altered content that has not yet been hashed. To combat this, platforms must incorporate additional tools such as:
AI and Machine learning: These technologies can analyze content in real time, identifying patterns or characteristics associated with CSAM, even if the material has not been previously flagged.
Keyword and metadata filtering: Tools that monitor text, filenames, or metadata associated with uploads can help flag potential CSAM for further review.
Real-Time monitoring: For live streaming or instant sharing features, AI-powered moderation tools are essential to detect and block harmful material as it is being shared.
Haris Kumar
Lead Content Strategist , CometChat