AI multiplied the speed of attacks. Model theft, data poisoning, and adversarial manipulation move faster than any security stack was built to answer. Seku.AI defends the model itself — and the data that shapes it — shipped as a platform for security teams and an API for the developers who build on top of them.
Runs inside your VPC. Prompts never leave your cloud.
Seku sits in front of your existing stack — OpenAI, Anthropic, open models, your own fine-tunes, any RAG pipeline. No re-platforming, no model migration. Route your calls through the Seku path and the entire perimeter is live in one line of code.
Detection got faster. Remediation for AI-native threats did not. For a compromised model driving financial or operational decisions, the time in between is where the damage happens.
Seku.AI shifts AI security from reactive patching to proactive defense — from the data that trains the model, through the prompts it answers, all the way down to its own weights.
The runtime perimeter for every prompt and response, running inside your environment.
Every other defense asks “is this data point anomalous?” — and every clean-label attack is engineered to answer no. Datakia asks “what is the causal effect of training on this batch?”
Policy and governance. Bring Shadow AI under zero-trust control.
Share and monetize proprietary models internally without risking your IP.
Federated deployment built to pass Fortune 500 vendor risk review.
The same engine that powers the enterprise platform, exposed as a clean REST API. Wrap a prompt, screen a dataset, or verify a model's provenance — without standing up any infrastructure.
Core endpoints
Mask PII, block prompt injection, and screen adversarial payloads in real time.
Score training data for its predicted effect on model behavior before ingestion.
Check a model's cryptographic provenance signature and confirm ownership.
Route a request to the most cost-efficient model that meets your quality bar.
Designed for developers who build with AI. Join the waitlist for early access when the platform launches.
Python, TypeScript, and Go clients with typed responses, plus a raw REST interface for everything else.
When you outgrow the API, the same primitives deploy inside your own VPC with SSO and audit logging.
A sandboxed replica of your live AI environment. Attack it, break it, and rewind it — without ever touching production.
Bombard the twin with prompt injections and jailbreaks continuously to surface vulnerabilities — never degrading the live model or polluting the real database.
When a breach or mass hallucination happens, replay the exact sequence against the twin to reconstruct the attack vector, find which safeguard failed, and patch it. Logs show input and output; the twin shows why.
Before pushing an update, run millions of simulated interactions against the twin baseline to catch catastrophic forgetting — stopping a bad update before it crashes a live system.
A poisoned batch is not an outlier. It is a cause. So Datakia stops looking for strange-looking data and starts measuring effects.
An attack B is a common cause of both the features and the label. It writes a trigger into X and dictates Y, opening a path the model happily learns:
A correlational learner cannot tell this apart from the real relationship X → Y. Datakia’s job is to deconfound — block the spurious path so only the true one survives.
Datakia learns the structure of your pipeline from provenance logs, gradient norms, and validation statistics — it is not a hand-drawn diagram. Crucially, the adversary’s intent is the one variable a defender never gets to log, so the discovery runs an algorithm that stays sound with latent confounders: it reports “something unmeasured is acting here” instead of inventing an edge that isn’t there.
For each incoming batch, Datakia estimates the causal effect of do(admit) — on clean-set accuracy and on statistical parity. The estimator is doubly robust, meaning it stays consistent if either the propensity model or the outcome model is correctly specified. You get a calibrated effect with a confidence interval, not an unfalsifiable anomaly score.
The response is proportionate to the predicted harm: admit at full weight, down-weight in the loss, quarantine for review, or block the source outright. And because the graph identifies the injection point, a confirmed compromise is rolled back surgically — the affected checkpoints, not the whole model.
Because accuracy and parity are modeled jointly, a batch that would quietly push your model past the Four-Fifths Rule is caught by the same gate that catches a backdoor. The bias review and the security review stop being two teams finding out in two different quarters.
A functional preview of cryptographic watermarking and Datakia’s causal screening gate. Simplified logic — enough to see how it works.
Select any model below. The scanner checks it against known ownership keys.
Simulate an adversary pruning weights from a watermarked model and watch whether the signature still verifies. (Illustrative model, not the production algorithm.)
Watermarked models retain accuracy and latency comparable to unwatermarked baselines.
Seku runs where your models run. The data plane deploys entirely inside your AWS, Azure, or GCP environment, so proprietary prompts never touch our servers — built to clear the Fortune 500 vendor risk assessment before the first call is ever made.
Deployment, SSO, and data residency details in the FAQ →The Governance Nucleus maps every deployment to the frameworks your risk team answers to — and turns them into real-time software controls at the model level.
Seku.
The details security and engineering teams ask before they commit.
You swap your model client for the Seku client and keep the same call signature. Point your existing calls at the Seku path — seku.chat(prompt) instead of your provider's call — and PII masking, prompt-injection defense, cost routing, and audit logging run automatically. No prompts are rewritten, no models migrated, and no pipeline is rebuilt. If you'd rather not change client code at all, Seku can also run as a transparent proxy in front of your existing endpoint.
The Seku data plane runs entirely inside your own AWS, Azure, or GCP environment as a single-tenant deployment. Your proprietary prompts and model traffic never leave your VPC and never touch Seku's servers. This federated model is built specifically to pass the Fortune 500 InfoSec vendor risk assessment — the review happens before the first call is ever made, because there's no external data path to review.
Seku is model-agnostic. It sits in front of OpenAI, Anthropic, open-source models like Llama, your own fine-tuned models, and any RAG pipeline.
Seku integrates natively with enterprise SSO via SAML, including Okta, Microsoft Entra ID (Azure AD), and Ping Identity. The Governance Nucleus enforces role-based access control (RBAC) over every active LLM connection across your organization, giving you a central point to discover and govern Shadow AI.
Threat logs and analytics live in a dedicated, single-tenant database that is fully isolated from any other customer. In a BYOC deployment, that storage sits inside your own cloud environment, so log data stays under your control and within your compliance boundary.
Developers can start on a free tier with generous request limits and no card required — enough to prototype and ship your first guarded call. Python, TypeScript, and Go SDKs are available, plus a raw REST interface. When you outgrow the API, the same primitives deploy inside your own VPC with SSO and audit logging, so you graduate to enterprise without rewriting your integration. For pricing on enterprise deployment, reach out through the form below.
The Governance Nucleus maps your deployments to NIST AI RMF, the EU AI Act, HIPAA, NY Local Law 144, and translates those requirements into enforced, real-time software constraints at the model level. It also generates the model cards and technical documentation regulators and internal risk teams require.
20 minutes. No obligation. Bring your hardest AI security question — or ask about developer API access.
