See how ActiveFence stacks up against other major security models. Get the benchmark.
Protect your AI applications and agents from attacks, fakes, unauthorized access, and malicious data inputs.
Control your GenAI applications and agents and assure their alignment with their business purpose.
Proactively test GenAI models, agents, and applications before attackers or users do
The only real-time multi-language multimodality technology to ensure your brand safety and alignment with your GenAI applications.
Ensure your app is compliant with changing regulations around the world across industries.
Proactively identify vulnerabilities through red teaming to produce safe, secure, and reliable models.
Detect and prevent malicious prompts, misuse, and data leaks to ensure your conversational AI remains safe, compliant, and trustworthy.
Protect critical AI-powered applications from adversarial attacks, unauthorized access, and model exploitation across environments.
Provide enterprise-wide AI security and governance, enabling teams to innovate safely while meeting internal risk standards.
Safeguard user-facing AI products by blocking harmful content, preserving brand reputation, and maintaining policy compliance.
Secure autonomous agents against malicious instructions, data exfiltration, and regulatory violations across industries.
Ensure hosted AI services are protected from emerging threats, maintaining secure, reliable, and trusted deployments.
Empower your team to make faster decisions with greater accuracy
Integrate one API to start using our AI-driven automated detection. Add risk thresholds aligned to your policy, so that high-risk items will be automatically removed and benign items ignored: allowing you to reduce violation prevalence while reducing human review to only those items that require it.
Send text, images, audio, or video for analysis based on our contextual AI models, fueled by intelligence of 150+ in-house domain and linguistic experts. For each item, our engine will generate a risk score between 1-100 indicating how likely it is to be violative, providing indicators and a description of the identified violations to make human decisions easier and faster.
Improve accuracy with a continuous, adaptive feedback loop that automatically trains our AI and adjusts risk scores based on every moderation decision.
ActiveScore child safety models automatically flagged a seemingly benign picture and description as high risk due to a promotion of a link to a malicious CSAM group with 67K members within the profile itself. By analyzing it against our intel-fuelled database of millions of malicious signals, including the profile’s complete metadata, the profile was immediately flagged to the platform and removed.
ActiveScore identified racial slurs in the review comments of a listing appearing to promote sales of artisanal soaps. By analyzing the post’s full metadata against 100+ languages, ActiveScore detected Spanish text as violative, saying: “Here comes Chaca down the alley killing Jews to make soap” and the review was automatically removed.
ActiveScore hate speech models automatically detected multiple white supremacist songs with media matching technology when compared to ActiveFence’s proprietary database that contains the largest database of hate speech songs. Within seconds, it found matched duplicates and similarities to provide a high risk score.
See how The Trevor Project proactively shield users from toxicity, reduces reliance on user flags, and improves efficiency with automation.
Uncover key trends in AI-enabled online child abuse and learn strategies to detect, prevent, and respond to these threats.
Explore the emerging threats that risk AI safety, and learn how to stay ahead of novel threats and adversarial tactics.