AI Security PostureManagement
Data asset risk, AI security posture, access identity governance. Three postures, one platform.
Discover AI assets. Map data pathways. Close sensitive exposures.
Models, agents, and knowledge bases scatter across the environment, and nobody can say who reads sensitive data or with which credential. AI-SPM connects them into one governable map: assets are objects, paths are links, governance is action. Humans can audit policy; an agent that crosses it surfaces as a risk.
AI Assets
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26 AI assetsQuestions to sort out when adopting AI
What AI assets are you running?
Which AI tools employees use, which AI apps teams have wired in, which homegrown agents are running. The security team has no inventory, and shadow AI becomes the default state.
- Continuously discover five ingestion faces: platform admin APIs, logs, endpoint probing, browser extensions, code hooks
- Bring all seven AI asset object types onto one ledger: models, agents, apps, endpoints, knowledge bases, pipelines, datasets
- See which data each asset touches and which identity it uses, pinpointed on the agent relationship graph
Where does data live, and how sensitive is it?
Data sits scattered across databases, object stores, cloud drives, and file shares, with no inventory of which parts are sensitive or where they live.
- 22 read-only data source types: column-level databases, object stores, cloud drives, file shares, and config centers
- The DataBrain engine classifies values, text, images, and documents, graded under GB/T 43697-2024 with parallel industry frameworks
- Near-duplicate documents sampled per cluster and the rest propagated, with 95% confidence bounds on coverage
How do AI assets access sensitive data?
Which route sensitive data takes into which AI asset: the security team has no map.
- Paths live in one graph ledger: data source, link, and AI asset, traceable segment by segment
- Data-to-AI chains identifiable and auditable
- Unapproved AI asset × sensitive data × exposed path: toxic combinations flagged directly
What identities do AI assets use to access data?
Service accounts, API keys, and tokens multiply as AI apps are adopted: over-granted rights unattended, dormant credentials unrotated, no lifecycle to speak of.
- AD, Kubernetes, cloud platforms, and config centers: 10 identity and event sources resolve to one ledger
- Identity → credential → resource → sensitivity tier, one four-link view
- Convergence candidates route straight to remediation, shrinking privileges toward least; credentials enter as fingerprints only, plaintext not stored
How are risks remediated?
Assessment reports keep arriving while exposures stay where they were: findings stop at the document, and remediation has no closed loop.
- Findings ruled by deterministic rules: reproducible and explainable, no manual write-backs
- Remediation triaged by severity: what hurts most goes first
- Remediation verified deterministically by the next scan cycle, logged through to closure
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How do you align with compliance frameworks?
Framework clauses on data classification and security audits ask, clause by clause, what data your AI touches and where the sensitive parts sit; a hand-assembled ledger turns audits into a scramble.
- Knowledge bases and database assets land in one compliance ledger, with sensitive columns and classification results cited directly
- The ledger and framework reports share one source, mapping clauses to assets and findings
- Classification follows GB/T 43697-2024 at the highest applicable level, and results are reused without re-labeling
Frequently asked questions
What is AI-SPM?
AI Security Posture Management: one platform that continuously discovers the AI assets in your enterprise, maps their connections to sensitive data and machine identities, assesses the risks, and drives remediation. AI-SPM is also the name of our AI security governance platform.
How is it different from cloud SaaS offerings?
AI-SPM is a private, single-artifact deployment inside your environment and can run air-gapped. Data, decisions, and reports all stay in-environment, for teams with hard requirements on data residency and network isolation.
Which AI platforms and data sources are covered?
AI usage across the enterprise, from coding agents to office assistants, is inventoried continuously and automatically; AI sources also cover platforms such as Dify and RAGFlow, vLLM and Ollama inference endpoints, and major vector stores; plus 22 data source types and 10 identity and event sources, all read-only.
Does sensitive data ever leave our environment?
No. Classification runs inside your environment; sensitive values are not stored or sent out. Credentials enter as fingerprints only; plaintext is not stored.
What does deployment look like?
A private single artifact. The full stack runs on one machine with as little as 8 GB of RAM and 4 vCPUs, and offline activation is supported.
Who is AI-SPM for?
Teams adopting AI platforms and agents who need to answer what data the AI touches: CISOs and security architects, data-governance and compliance leads.
Three postures. One platform.
Book a demo to see how data, AI, and identity connect into one governable map.
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