AI SecDevOps
Ship models as carefully as you ship code.
Machine learning has moved from experiment to production, and the pipeline that carries a model now carries your risk. Training data, weights and GPU capacity are production assets. Yet in most teams they are still handled outside the controls that already govern application code.
NeoTechAdmin builds and runs both halves: the secure delivery pipeline, and the managed platform underneath it. One team owns CI/CD, infrastructure as code, monitoring, security and compliance, so your engineers can work on the model instead of the machinery.
What AI SecDevOps covers
Four areas, and we take on as much or as little of each as you need.
Pipelines
Continuous integration and delivery for code and models alike. Every change is version controlled, tested and promoted along the same reviewed path, so a model reaches production the way a release does rather than by hand.
Provenance
Datasets, weights and container images are tracked back to their source. You can say which data trained which model, which image is serving it, and exactly what changed between one version and the next.
Platform
Managed infrastructure and platform as a service. We take ownership of servers, storage, networking, virtualization and GPU capacity, so your team gets compute and scale without running hardware.
Assurance
Monitoring, audit logs and evidence collected as you go. The result is reporting an auditor or a customer’s security questionnaire will accept, including SOC 2 readiness.
Why AI changes the pipeline
A model is not a binary. Instead, it is a set of weights produced by data you may no longer hold, on hardware you rented, using libraries that have since moved on.
What breaks first
Reproducibility goes first. Then provenance: when someone asks which data trained the model behind a decision, the answer has to already exist. Then cost, because idle GPU capacity is the quickest way to lose a budget.
Meanwhile the security surface widens. Training data is often the most sensitive material a company holds, and it now moves through build systems that were designed for source code.
Consequently the controls you already trust for applications — version control, review, automated testing, least privilege, audit logging — have to reach the model pipeline too. That, in short, is AI SecDevOps.
The managed platform
With managed infrastructure as a service, we take full ownership of the layer beneath your workloads: servers, storage, networking and virtualization. As a result your team gains compute and scale without the burden of running physical or virtual hardware.
Managed platform as a service goes a step further, providing a fully configured, maintained and monitored environment in which your developers and operations teams build, deploy and scale. Whether you are migrating legacy systems, consolidating data centers or modernizing a delivery pipeline, we provide the expertise and the round-the-clock management.
Automation is handled with tooling your team already knows: Terraform, Ansible, Git, SaltStack and application performance monitoring.
Security and compliance, automated
Security is not a stage bolted on at the end. Rather, we audit and analyze your implementations continuously, and audit logging is automated, so risks surface while they are still cheap to fix.
Furthermore, we work with clients in healthcare and financial technology, integrating SaaS on virtualized and containerized infrastructure. The controls we apply are therefore the ones regulated industries already expect to see.
Tell us about your stack
No two environments are alike. Tell us what you run, what you are training and what you have to prove to an auditor, and we will map the shortest route from there.
Our consulting is delivered by an agile team, at a fraction of the cost of hiring the same skills internally.