Multimodal Risk Detection
Detect text, image and contextual risks with optional local hard constraints.
AIGC content security
An operational loop for multimodal risk detection, policy decisions, human review, sample feedback and audit across generated content.
Core Risks to Address
Generated content creates regulatory, bias, discrimination or brand risk
Variants, euphemisms and multimodal inputs bypass moderation controls
Manual review cannot match real-time dialogue and content scale
Policy, review, appeal and sample feedback remain disconnected
Core Capabilities
Policy Decision Engine
Configure allow, block, replace or review actions by business and risk level.
Human Review Operations
Support review, sampling, appeals, label correction and quality evaluation.
Security Gateway Integration
Use standard APIs or compatible gateways for unified input and output checks.
Typical Scenarios
- Customer service, digital humans and conversational bots
- AIGC copy, image and marketing-content platforms
- Community comments, user submissions and interactive content
- Input/output governance for public LLM applications
Delivery Scope
- Content-risk taxonomy and policy baseline
- Content-security platform, review APIs and business integration
- Human review, sampling and sample-feedback workflow
- Compliance audit, effectiveness metrics and operations reporting
Delivery process
Ingest content
Define content types and review entry points.
Evaluate risk
Apply content policies to detection results.
Review and act
Connect human review, feedback and exception handling.
Feed back samples
Retain review outcomes to inform policy improvements.
Deliverables
Scope, schedule and acceptance criteria are agreed following assessment.
Discuss this approachDeliverables to scope together
- Scenario and risk-test records
- Integration, policy configuration and validation notes
- Operational audit and improvement actions
Explore further
JuHeng
Connect content review, policy decisions and ongoing operations
LLM Application Security
A unified security control point for prompt-injection defense, input/output inspection, sensitive-data protection, access control and model-call audit.
AI Security
Lifecycle security for LLM, AIGC, RAG and agent applications across data, model, application and operations layers.