Seven layers of enterprise grade security.
Every interaction runs through a defined sequence of controls — each one designed, deployed, and audited on its own terms, so no single safeguard carries the weight alone.
AI policy enforcement
A configurable policy engine ingests your firm’s AI-usage policies, ethical guidelines, and regulatory constraints, then applies them to every input and output as a gate rather than a guideline. Policies change without retraining a model, and every decision is written to an audit trail.
PII redaction
Mandatory pre-processing detects and removes personally identifiable information, non-public information, and financial identifiers inside your environment — before any payload is sent to an LLM. What the model never receives, it can never expose.
Guardrails
Dedicated filters inspect user inputs and model outputs, kept deliberately separate from policy so each can evolve on its own. Inputs are screened for prompt injection and abuse; outputs are checked for grounding, toxicity, and format before they ever reach a client.
Evals & observability
Turn-level logging and automated evaluation give full visibility into each interaction. Scores are computed continuously, and anything below the thresholds you set is flagged for review the moment it happens — not discovered in an audit months later.
Deployment sovereignty
The customer-specific stack deploys into your on-premises data center, an air-gapped environment, or a dedicated single-tenant cloud managed for you alone. Proprietary data stays isolated with its own compute, storage, and endpoints; residency is set to your jurisdiction, and no data leaves without your consent.
Role-based access control
Access minimization runs throughout the platform. Users — and the agents acting on their behalf — reach only the data and actions their role and use case require, all administered centrally and logged in full.
Encryption at rest and in motion
All data is encrypted with industry-standard algorithms throughout its life — moving between services and sitting in storage — with key management you can hold yourself.
AI you can put in front of a client.
The hardest problem in applied AI for a fiduciary is not capability — it is accountability. Pravar is built so the machine carries the load and a person carries the judgment.
Nothing sensitive gets through
Sensitive data is redacted inside your environment before any model receives it, and every input is screened for injection and abuse. What the model can’t see, it can’t leak.
A human owns what matters
Agents draft, propose, and prepare; consequential actions wait for review. Human judgment — trust, empathy, accountability — stays with your people by design, not as a fallback.
Nothing is taken on faith
Every turn is evaluated, scored, and logged on one execution path, so behavior doesn’t drift between test and production and every action can be explained after the fact.
A note on what this page claims
The controls described here are architectural commitments of the Pravar platform. Named standards — SOC 2 Type II, HIPAA, GDPR, India’s DPDP Act, the UAE’s PDPL, and ISO/IEC 42001 — indicate the frameworks our controls are designed and mapped to; they are not a claim of current certification. Formal certification is in progress and not yet complete. Deployment-dependent capabilities vary by configuration. Current attestations, control mappings, and roadmap are available under NDA.