Bolt for your role
Bolt by Sparcle · Governance, Data Sovereignty & Compliance · Empower · Govern · Audit
Bolt by Sparcle · Governance, Data Sovereignty and Compliance
The crux
Your people are already using AI on your data. The only question worth asking is whether that use is one you can control , one you can see , and one you can prove . Sparcle exists to make the answer yes.
AI adoption is outrunning AI control
The productivity is real and unstoppable. The governance to match it is not in place, and the liability still sits with you.
Roughly half of employees use AI tools no one approved, and senior staff are the heaviest users. Every paste of a customer name or account number is an ungoverned data transfer.
In breaches involving AI, 97% of organizations lacked proper AI access controls, and 65% of shadow-AI incidents exposed customer PII.
Regulators, boards and customers hold the organization responsible, not the model vendor. You are asked to prove what your AI touched, and where it went.
Governance today is documented, not enforced
Most AI governance is documented, not enforced
The tools on the market split into two camps, and both leave the same gap open.
Policy packs, AI registries, model cards and risk registers describe intent. They sit beside the data flow, not in it. Nothing stops the next prompt.
Cloud proxies and cloud assistants that do enforce inspect your prompts inside their cloud, or only within one vendor's ecosystem. Your data still leaves the boundary you control.
No dominant player enforces in the data path, inside your perimeter, in front of any model you choose. That gap is exactly where Bolt sits.
Empower, Govern, Audit: one operating model
One operating model: Empower , Govern , Audit
Each verb protects the other two. Govern without Empower gets bypassed by shadow AI. Empower without Audit is indefensible. Together they are how responsible AI actually holds.
Give the whole workforce real AI leverage, across every system, so no one needs an unsanctioned tool. Most everyday work resolves on-device, at zero tokens.
Every use crosses a boundary you set: PII masked before the model, access scoped to identity, capabilities and models under admin control.
Every action is recorded in a tamper-evident log you can verify offline. You can answer "what did AI touch?" the moment you are asked.
You define the boundary
You define the boundary. Bolt enforces it .
Air-gap, a private model endpoint, or a public model with masking on. You pick the posture per deployment. Sparcle is never in the loop.
Helm chart or Docker in your Kubernetes cluster, VPC, on-prem server, or air-gapped network. Lives entirely inside your perimeter.
Bring any model: a private endpoint on your GPUs, a local open model, or a public API. Admins can set an org privacy floor that refuses any model whose endpoint sits below it.
Bring your own keys, vaulted with per-tenant encryption. Swap models or providers without re-platforming.
Sensitive data is masked before the model
Sensitive data is masked before it reaches the model
One chokepoint on the LLM boundary, always on for structured identifiers, independent of which model you picked. Personal and health identifiers are detected, masked and pseudonymized before any prompt leaves your boundary, across streaming responses and text inside images, then restored on-device in the answer. Enforced in the path, not written in a policy.
Detect personal and health identifiers in the prompt, contacts, financial and national IDs, validated with real check-digit algorithms so look-alikes are not flagged. Text inside attached images is scanned by OCR through the same detectors.
Replace each identifier with a stable, reversible pseudonym before the prompt leaves your boundary. The model reasons over the request, and on an unknown checksum the pipeline fails closed.
Map the pseudonyms back to the real values inside your perimeter when the answer returns. The real values stay inside the boundary you control.
Policy and human oversight
Who can use which systems, models and capabilities is set by your directory, and the consequential actions wait for a human. Nothing is left to the honor system.
Access scoped by role, department and group from your IdP over SCIM or SAML. Every action reaches each system as the user's own identity, never a shared bot.
Enable, disable or restrict capabilities by role or group, connectors, models, clipboard, screen capture, with a kill-switch on each.
Capture, simulate against your user population, activate, and roll back by pointer. Change the rules without fear.
Send, delete, schedule and pay wait for explicit human sign-off, a durable request that survives a restart, so nothing consequential auto-fires.
Govern the AI your people already use
The shadow-AI problem needs an answer, not just a policy. Bolt puts the traffic from the AI tools your people already reach for through the same masking, policy and audit boundary, so their convenience stops being your exposure.
Third-party AI clients route through Bolt. Prompts, tool calls and image content are masked, checked against policy, and logged before anything leaves your boundary.
On paste, or as your people type into public AI sites, sensitive data is detected and redacted in place, and can be blocked outright under a managed policy.
Every mask, allow and deny on that traffic seals into the same audit chain. Shadow AI becomes governed AI you can prove.
The framework map
The same controls, mapped to every framework
Buyers now expect this mapping as table stakes. The differentiator is that on Bolt these are running controls, not documented intentions.
One engine, any jurisdiction
Regulations ship as signed, swappable packs that feed the same masking chokepoint. A new country is one pack, not a new product.
Packs across major jurisdictions, GDPR, UK GDPR, DPDP, CCPA, LGPD, PIPL, APPI, PIPA and more, each declaring its identifiers, rights and residency.
National IDs are checked with their true check-digit algorithms (Aadhaar, PAN, GSTIN, NHS, Singapore NRIC, Australia TFN, Brazil CPF and CNPJ, Japan MyNumber, Korea RRN and more), so look-alike strings do not trigger false masking.
Packs are cryptographically signed bundles, reviewed on install, activated per organization. Author a new jurisdiction without touching the engine.
DPDP done right: the processor leg
Govern the point of use, the leg no consent manager can see
Consent is captured at your front door by your consent manager. Bolt sits downstream, governing the internal leg where your people and your AI actually touch that data.
Rights and retention
Erasure, retention and key destruction are wired as running jobs, mapped to the rights each active regulation declares.
A typed cascade erases a subject across messages, sessions and pseudonym tokens, tenant-scoped so one erasure never crosses into another tenant, and revokes the user's upstream OAuth grants so a deleted user keeps no live access.
Messages, sessions, runs and audit entries age out on the retention window you set, enforced daily, not left to manual cleanup.
Destroy a tenant's key and its data becomes unrecoverable, the strongest form of erasure. Each erasure emits a signed, offline-verifiable deletion receipt sealed into the audit chain when KMS is configured.
Where your data lives
The workforce, the engine, the masking, and the store all sit inside a boundary you own. Only masked prompts cross to the model you selected. Sparcle has no server in the path.
Encryption and key control
Data at rest is encrypted under per-tenant keys you control, so possession of the disk is not possession of the data.
Envelope encryption with a customer master key per tenant, backed by a local KMS, AWS KMS or HashiCorp Vault. Bring your own keys.
OAuth and model credentials sealed with authenticated encryption, bound so a token cannot be lifted and replayed for another user.
Rotate keys on schedule; shred a tenant key to make its data unrecoverable. Each shred emits a signed, offline-verifiable deletion receipt, so erasure is proven, not just asserted.
The safety net
Even the most permissive choice stays safe
The question every CISO asks: "if my analyst picks a public model, does sensitive data leak?" The answer is no, because masking happens before the call, regardless of the model.
Proof, not just a log
A record you can verify, without trusting us
Most tools give you a log you have to take on faith. Bolt gives you a cryptographic proof of integrity that a regulator can check with a standalone verifier, needing neither our database nor our keys.
Events are hashed into a Merkle tree and the root is signed by your key. Altering any past entry breaks the proof.
Export an evidence bundle that verifies on its own. No live connection to Sparcle, no dependence on our infrastructure to prove it.
Competitors hand you a log. Bolt hands you proof. For a compliance buyer, that is the difference between an assertion and evidence.
Accountability at every layer
Governance is not one gate. It is a stack of controls, each enforcing something and each emitting evidence, so no path can skip a rule and every question has an answer.
Transparency and evidence you can hand over
The evidence you hand an auditor or a board
Transparency is not a dashboard you hope is right. It is a record you can produce, filtered and signed, the moment you are asked.
Method, path, user, status, duration and correlation IDs on every API call, with credential paths redacted.
Export the safeguards and audit history for a specific regulation, labeled and ready for an assessment or inquiry.
"What did our AI touch, for whom, and where did it go?" is a query, not a research project.
An org-wide read of whether masking and policy cover every surface, and whether any config has gone stale, so assurance is measured, not assumed.
The posture you can attest to today
We don't sell you a certification. We give you the running controls that get you certified, and we're precise about what's always on, what your admin turns on, and what's still roadmap.
Give your workforce AI you can stand behind
Enforced, in your perimeter, in front of any model. Empower the whole workforce, govern every use at a boundary you control, and audit it with proof you can hand over, without surrendering control or visibility to a cloud provider.
See it on your own data
Bolt runs inside your perimeter, on the LLM you choose.