AI-SPM
AI Security Posture Management
Inventory AI components, owners, data paths, controls, findings and risk context across the AI application lifecycle.
AI-native security, human-governed
Secure the application layer that traditional scanners cannot see.
Extend assurance into LLM, RAG, model, vector, agent and tool behavior while connecting AI posture to application, cloud, asset and runtime context.
AI can assist with insight. Evidence, policy and authorized workflow remain the trust layer.
The operating model
Security for systems that reason, retrieve and act
Modern AI applications add attack surfaces across prompts, system instructions, retrieval pipelines, embeddings, vector stores, models, agents, tools, memory, identity and downstream actions.
EnProbe's AI security direction connects these risks to the application, data, cloud and owner context already present in the platform, rather than creating an isolated AI finding queue.
Core capabilities
What the platform brings together.
Every capability shares evidence, identity, context, workflow and audit instead of producing another disconnected queue.
LLM
LLM application testing
Evaluate prompt injection, unsafe output handling, sensitive information disclosure, excessive agency and model interaction risk.
RAG
Retrieval security
Assess poisoning, authorization, source trust, embedding leakage, vector-store exposure and context manipulation.
AGENT
Agentic security
Test tool permissions, goal manipulation, memory, identity, delegation, action boundaries and multi-agent trust paths.
RFP
Runtime Fabric Protocol
Correlate approved runtime sensors and signed organizational telemetry to cloud and application assets.
COPILOT
Explainable security assistance
Use AI to summarize, correlate and recommend while authoritative state remains evidence-based and auditable.
How it works
From evidence to owned action.
- 01
Map the AI system
Identify models, prompts, data sources, vector stores, agents, tools, identities, owners and environments.
- 02
Exercise trust boundaries
Test deterministic controls and adversarial behaviors across reasoning, retrieval and action layers.
- 03
Correlate business context
Connect AI findings to application exposure, data sensitivity, cloud posture and runtime relationships.
- 04
Govern the outcome
Preserve evidence, explain limitations and require authorized decisions for verification and risk acceptance.
Expected outcomes
What changes when the evidence is connected.
- AI risks are connected to the application and business service they can affect.
- LLM, RAG and agentic findings share the same ownership and remediation discipline as other application risk.
- Automated assistance cannot silently alter authoritative status or access decisions.
- Runtime evidence can progressively improve reachability and confidence without exposing prompts or secrets by default.
Frequently asked
Questions about ai-native security, human-governed.
What is AI-SPM?
AI Security Posture Management is the continuous inventory, evaluation, context, prioritization and governance of security risk across AI systems and their supporting components.
Can traditional SAST and DAST cover AI applications completely?
No. They remain important for conventional code and runtime behavior, but AI applications also require testing of prompts, retrieval, models, agents, tools, memory and action boundaries.
Can AI decide that a finding is safe?
AI may assist with classification and recommendations. Authoritative closure, risk acceptance, access and verification should remain policy-driven, evidence-based and auditable.
The bottom line
EnProbe turns disconnected scanners, cloud signals, asset data and expert findings into one explainable, prioritized and verifiable security program.
Discover what exists. Test what can fail. Understand what matters. Fix it with the right owner. Retest it with evidence. Prove that risk is being reduced.