More data for your AI. Less exposure for your business.
Sensitive data is classified and protected at ingestion; access is controlled at retrieval; and every interaction is logged for auditing across RAG pipelines, copilots, fine-tuning, and agentic workflows.
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Same question. Different answer.
Switch users and watch AI Guard enforce your policy in real time. Same knowledge base, different results based on who's asking.
Your AI knows too much.
Or nothing at all.
Every RAG pipeline, copilot, and fine-tuning job is a direct line into your sensitive data estate. Most organizations either block AI access entirely or let it ingest everything unfiltered. Both are a problem.
What happens today
Documents flow into AI pipelines unfiltered. PII, PHI, privileged communications, deal terms, and trade secrets are embedded into vector databases in clear text.
What AI Guard changes
Policy sets what gets protected and how: encryption or tokenization. Embeddings are obfuscated and stay searchable. Your AI works with more of your data, but agents see only what their identity and role permit.
Source to retrieval. Protection never leaves.
Connect your sources, classify sensitive spans, apply selective protection, and enforce least-privilege access at query time, in one unified pipeline.
Connect
Point AI Guard at your vector database: Amazon S3Vector, Pinecone, Weaviate, Chroma, Qdrant, LanceDB, or CosmosDB. Integrates with standard AI pipeline interfaces.
Discover & classify
Scans and identifies sensitive information, including PII, PHI, IP, and regulated data, with confidence scoring.
Encrypt, redact, or tokenize
Protection at the granularity each field needs: word, page, or document. Embeddings obfuscated, semantics preserved.
Enforce at retrieval
Authorized identities get the real value back inline, decrypted or detokenized by policy. Everyone else sees the surrounding context only.
What makes AI Guard different
Not another classification tool. Not a perimeter control. Protection that sits at the document level and travels with the data wherever AI takes it.
One policy across every AI workflow
RAG, fine-tuning, inference, and agentic ops under one framework, not three tools. Protection runs from source through embeddings through agent responses.
Models read around protected content
Vector matching and model performance are preserved even when embeddings are obfuscated. Field-level across every AI workflow, not a security-versus-usability tradeoff.
Least privilege for agents, not just users
Agents are treated as non-human identities with explicitly scoped, auditable access. Attribute-based access control at the content level, not just role-based access control.
The perimeter is the content itself
Encryption follows the file into unauthorized AI tools, misrouted pipelines, or breached vector databases. Portable and cloud-agnostic.
Billions of records, accuracy intact
Protect at scale while preserving search accuracy and AI usability. Standard interfaces drop into existing pipelines without re-architecture, across multi-cloud and hybrid.
Protected today, protected after Q-Day
Future-proof cryptography built in across clouds, storage, and AI pipelines. No re-encrypting the estate later.
The moments where AI data exposure becomes real
Four situations where no protection means a breach, a compliance failure, or a deployment that never gets approved.
You provide deal memos, contracts, and HR files for your new RAG model. No one checked what it ingested.
By the time legal hears about it, the model has embedded three years of sensitive documents into a vector DB in clear text. Now any user querying your company's AI tool could be fed this information, regardless of clearance level.
Every sensitive element is classified before ingestion and embeddings are obfuscated. Analyst queries return context, not PHI or privileged content. Compliance evidence is generated automatically.
You fine-tune on clinical data. The model trains on real patients and starts reproducing them.
Training data with PHI gets baked into model weights. Targeted prompts can extract it. Regulators don’t care that the exposure was unintentional.
The dataset is sanitized before the job runs, tokenizing and masking sensitive fields while preserving the clinical structure the model learns from. Raw values never enter the pipeline.
A hidden instruction in an email makes your copilot retrieve files and exfiltrate them.
Exactly what happened to Microsoft 365 Copilot (CVE-2025-32711, June 2025). Perimeter controls saw nothing, because the activity moved through approved channels. Preventing prompt injection is one focus. Limiting the damage that a successful prompt injection can wreak is another.
Sensitive spans are encrypted at the content layer, decryptable only by authorized identity. The injection still triggers retrieval — but the copilot surfaces ciphertext, not readable data. Protection travels with the content.
Your DLP blocks employees from uploading files to AI tools on company devices. So they use their personal computer instead.
A contract, financial model, or client record gets dropped into an unapproved AI tool. That tool may train on it, and your client's private information becomes part of globally accessible model outputs.
Content-layer protection travels with the sensitive information. External tools can process the non-sensitive context around protected fields, so contracts, financial models, and client records can still be used in AI queries and workflows without risking sensitive data becoming a statistic.
The gap other tools leave open
DSPMs classify. Vector-DB vendors protect embeddings. The only unified and preemptive solution.
What security teams ask us first
Doesn’t encrypting data break our AI models?
No — because AI Guard uses semantic-preserving encryption. Sensitive spans are encrypted at the field level, but the surrounding context stays accessible to the model. Vector similarity is preserved in obfuscated embeddings, so retrieval accuracy is unaffected. The model sees what it needs to be useful; it just can’t read the sensitive fields.
How is this different from what our DSPM already does?
DSPMs classify and report — they tell you what sensitive data exists and where. They don’t protect it or enforce access in AI workflows. AI Guard is the enforcement layer: it takes the classification signal, applies cryptographic protection before data enters the pipeline, and enforces per-agent policy at retrieval. Discover or your existing DSPM can both feed it.
Can AI Guard handle agentic workflows, not just RAG?
Yes. AI Guard treats agents as non-human identities with explicitly scoped access. Per-agent policies enforce least privilege at the span level — agents retrieve only what their identity and role permit, with every interaction logged. This works across RAG pipelines, fine-tuning jobs, and agentic inference.
What happens to data already in our vector database?
AI Guard can retroactively protect existing vector stores by re-indexing with obfuscated embeddings. For new ingestion, protection is applied before documents enter the pipeline. We recommend the free AI risk assessment first — it surfaces exactly what sensitive data is currently sitting exposed in your knowledge bases.
Who controls the encryption keys?
You do. Confidencial never sees plaintext. Keys are managed in your environment — bring your own KMS or use AI Guard’s built-in key server. Access is identity-bound through your existing IdP, so revoking an agent’s or user’s access is a policy change, not a re-encryption job.
How does AI Guard prove compliance to auditors?
Every event — ingestion, classification, encryption, access, query, and response — is logged at the span level with tamper-proof audit trails aligned to GDPR, HIPAA, PCI DSS, ISO 42001, and NIST AI RMF. Auditors get intra- and inter-component tracing that legacy DLP and RBAC tools can’t produce. Compliance evidence builds itself as the system runs.
Your AI is only as trusted as the data it can access.
AI Guard enforces what each user and agent can retrieve at the data layer, before the model sees it.