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.
Cut cloud AI cost with intelligent routing and hard spend caps.
License proprietary models and agents — internally or to the market — without ever risking your IP.
Federated deployment.
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.
When the Kwatuo Engine detects a zero-day vulnerability in the sandbox, the Zoba Module automatically generates the appropriate security patch and deploys it across the environment, proactively immunizing all affected systems against the threat..
A functional preview of cryptographic watermarking, Datakia’s causal screening gate, and the FinOps AI router. 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.
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. When you’ve built something good, license it to the enterprises that need it.
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.
Generous request limits for prototyping. No card required to get a key and ship your first guarded call.
Python, TypeScript, and Go clients with typed responses, plus a raw REST interface for everything else.
Ship a model or an agent to the Sovereign Exchange and license it to enterprises — watermarked, red-teamed, and compliance-checked before it ever goes live. See how →
Enterprises are rebuilding the same models, datasets and agents in parallel, badly, because they can’t trust anyone else’s. Seku.AI removes the reason for that distrust — every asset on the Exchange is watermarked, red-teamed, and mapped to the regulations its buyer answers to. So specialists can sell what they know, and enterprises can stop building everything from scratch.
You do not need to be an engineer to build an agent on Seku.AI.
List a model, an agent, or a governed dataset. Lease it, rent it per query, or sell it outright.
Enterprises no longer have to build every model, agent, and dataset in-house.
A marketplace for AI assets is an obvious idea, and it has not worked, because no buyer will run a stranger’s model against their production data and no seller will hand over their weights. Seku.AI solves both halves of that at once: the seller’s IP is protected by watermarking and double-blind serving, and the buyer’s risk is bounded by red-teaming, causal data screening, and regulatory mapping. Remove the distrust and the market appears.
Seku runs where your agents, datasets and models run. The data plane deploys entirely inside your AWS, Azure, GCP or On-prem environment, so proprietary prompts never touch our servers.
Deployment, SSO, and data residency details in the FAQ →The Compliance Nucleus maps every deployment to the frameworks your risk team answers to — and turns them into real-time software controls at the data, agent and model level.
Seku.AI is built by cybersecurity & AI engineers specializing in AI security, with track of powering and securing generative AI applications across finance, healthcare, logistics, oil & gas and enterprise software.
The details security and engineering teams ask before they commit.
You swap your model, data and agent clients for the Seku client and keep the same call signatures. 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, GCP or On-prem environment as a single-tenant deployment. Your proprietary prompts, agent and model traffic never leave your VPC and never touch Seku's servers. This federated model is built for enterprise privacy.
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. The FinOps Router can route a single request to the most cost-efficient model that meets your quality bar — sending simple tasks to efficient open models and complex tasks to frontier models — with no code changes to your application.
Seku integrates natively with enterprise SSO via SAML, including Okta, Microsoft Entra ID (Azure AD), and Ping Identity. The Compliance Nucleus enforces role-based access control (RBAC) over every active LLM, agent and data 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 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 Compliance Nucleus maps your deployments to NIST AI RMF, the EU AI Act, HIPAA, NY Local Law 144, Executive Order 14110, and almost all the established regulations and frameworks around the world and translates those requirements into enforced, real-time software constraints at the model, data and agent level. It also generates the cards and technical documentation regulators and internal risk teams require, and SOC 2 is in progress.
20 minutes. No obligation. Bring your hardest AI security question — or ask about developer API access.
