We're a small team. Is this premature?▼
The cheapest time to set this up is before you have many people, datasets and customers. A handful of controls prevent most early-stage incidents: SSO with strong MFA, managed laptops, a secrets manager, public-access blocks on storage and authentication on notebooks. Those take days, not months. Retrofitting them later, after keys have been shared in chat and datasets copied everywhere, is much harder. We start small and add controls as you hire and sign customers.
How do you protect model weights?▼
Weights are usually files in object storage, a model registry or on training nodes, so we protect them like any high-value file: restrict who can read and write them, block public access, enable versioning and logging, and alert on unusual large downloads. We also limit which laptops and identities can reach that storage at all. If you serve models to customers, we work with your engineers on keeping serving infrastructure separate from research storage.
What is the NIST AI RMF, and do we have to follow it?▼
The NIST AI Risk Management Framework is a voluntary framework for identifying and managing risks from AI systems, organized around governing, mapping, measuring and managing risk. It isn't a law, but enterprise buyers and some government customers use it as a reference when evaluating AI vendors. We use it to shape practical policies on data sourcing, access and release. Whether specific laws, such as the EU AI Act, apply to your product is a question for your counsel.
A customer wants us to delete their data after a pilot. Can we prove it?▼
You can if you know where every copy lives. That's why we set up labeled, access-restricted storage for customer data, log access and keep it out of shared buckets and general training pipelines unless the contract allows otherwise. When the request comes, you delete from the known locations, check backups and experiment artifacts, and record what was done. Whether derived models must be retrained is a contract and legal question for your counsel.
Do you secure our AI product itself?▼
Our focus is the corporate environment and infrastructure access: identities, devices, cloud permissions, secrets, data storage and logging. Product-level issues like prompt injection, output filtering or model abuse are application security work your engineers own, though we're glad to point out where infrastructure controls help, such as isolating serving systems and protecting keys. For deep application testing, we recommend a specialized assessment and can help you scope it. That keeps responsibilities clear on both sides.
Can you help us get through investor technical diligence?▼
Yes. Investors increasingly ask about data rights, access control, key management, incident history and whether customer data is used for training. We help prepare a short, accurate security overview, gather supporting evidence like access reviews and device reports, and join calls when your CTO wants backup. We won't overstate what's in place, because diligence answers can end up as representations in deal documents. Accurate and modest beats impressive and wrong when lawyers are reading.
Do you provide AI Startups IT Services in Houston and nearby areas?▼
Yes. LayerLogix is based in the Greater Houston area and delivers AI startups IT services to businesses across Houston and the surrounding communities, including The Woodlands, Spring, Katy, Sugar Land, Conroe, Cypress, and Pearland. For most Houston-area clients we can be on-site the same day when something needs hands-on attention, and our help desk is available during business hours, with after-hours emergency support. Call 713-571-2390 to check coverage for your specific address.
What does AI Startups IT Services cost for a Houston business?▼
Pricing depends on your size and what you need, so we do not publish a one-size-fits-all number — but Houston businesses generally pay a flat, predictable monthly fee rather than surprise hourly bills. We start with a free, no-obligation assessment of your current setup, then give you a clear quote in plain English with no hidden costs. That way you know exactly what you are getting and what it costs before you commit.