Complete Ecosystem Visibility
AI systems are becoming a way into your broader IT infrastructure. AI.Guard provides a multi-layered defense system extending beyond just the models themselves, encompassing datasets, access rights, and environments.
AI-BOM Inventory
Maintain a complete inventory of models, datasets, and GPU-driver versions. Track patching levels of inference servers to ensure foundational security.
Environment Coverage
Secure infrastructure, networks, and storage used in your AI deployments, ensuring the environments hosting your models are tightly controlled.
Access & Rights Management
Restrict AI-agent privileges and ingest access-rights data from widely used knowledge stores (e.g., Confluence, Wiki) to prevent unauthorized data exposure.
Dataset Protection
Guard training datasets, knowledge bases (RAG), and vector databases against tampering and poisoning attacks.
Anomaly Monitoring
Analyze token-consumption patterns to protect against Denial-of-Wallet (DoW) loops, and flag anomalous request types in real time.
Web-search perimeter
Create a multi-layered barrier between corporate LLMs and the open internet, featuring input data validation and outgoing flow filtration.
Local LLM Web-Search Protection
When internal models need access to real-time external data, the perimeter expands. AI.Guard introduces strict controls for web-connected local LLMs.
1Input Data Validation
Content scanning, filtering by category, and strict verification of incoming external data structures and formats.
2Outgoing Flow Filtration
Data filtering, masking, and anonymization before the search query leaves the perimeter. Limits the number of outbound requests.
3Access Policies
Configuring roles and implementing corporate information security policies for search, limiting queries to strictly defined sources.
4Search Monitoring
A dedicated log of all search queries and their retrieved results, generating immediate alerts for suspicious outbound activity.
Model layer + agent layer
One threat landscape, two enforcement points
AI.Guard addresses OWASP LLM risks at the prompt, response, data, and model boundaries. Autonomous-agent risks require a local execution control: Agent Guard observes reasoning and evaluates tool and MCP actions before execution.