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AI security for manufacturing

AI security for manufacturing: agents on the plant floor, IP off the table

AI security for manufacturing starts at the IT/OT line. Maintenance copilots, quality agents and supply-chain helpers live at the IT/OT line. Most plants have no governance function there at all. The data they touch is the crown jewels: process settings, supplier pricing, engineering records, operator names.

DataShield governs what an agent can reach, rather than trusting what it will do. We tokenize sensitive fields at ingest. We scope every governed tool call to an agent identity. You can revoke mid-session. A tamper-evident audit chain shows which agent touched which dataset.

Calculate your ROI Verify a sample audit chain yourself

OT data protection for AI agents: three exposures, three mechanisms

Process IP leaking into prompts

Recipes, tolerances and supplier terms are trade secrets. But only while you hold the keys to them. DataShield sorts and tokenizes sensitive fields at ingest, with deterministic, join-preserving tokens. A copilot can tie downtime to one supplier's parts. It never sees that supplier's name or prices. See Ontology.

Agents with plant-wide reach

An agent built for one line should not read the whole historian. Every governed tool call passes three gates. Token scope ceiling. Authority tier. A fresh check that the session is still live. Then we meter it and dispatch it. A handed-down key can never carry more scope than its owner meant to give. See Auth.

No record for auditors or customers

Defense primes and car makers now audit how their suppliers use AI. A SHA-256 hash chain with Ed25519-signed checkpoints gives you per-call proof of what each agent read. A check tells you if someone altered, added to, cut, or trimmed a log's end. Run it on a sample log.

NIST AI RMF in manufacturing, and CMMC when AI tools touch CUI

The NIST AI Risk Management Framework asks plants to govern AI across upkeep, quality and supply chain. Its Generative AI Profile (AI 600-1) names the shop-floor risks by name. Prompt injection through an operator screen. Made-up repair steps. Model drift, after someone changes a machine. The govern-map-measure loop needs one input above all. A record you can trust: what the AI did, and with what data.

Defense-adjacent plants carry a harder rule. Under CMMC Level 2, AI tools that handle CUI must meet the 110 controls of NIST SP 800-171. That includes access control, least privilege and audit accountability. An agent stack with open data access and editable logs fails on sight.

DataShield runs self-hosted, on your network. No cloud hop between your agents and your process data. Your keys stay in your hands. That is the model OT sites ask for. Read the architecture and the security posture, threat model and key handling included.

The cost of ungoverned AI over CUI and IP When AI tools touch CUI they inherit CMMC Level 2, its 110 controls assessed by a third party rather than self-attested. The NIST AI RMF Generative AI Profile (AI 600-1) names industrial risks: prompt injection through operator interfaces, hallucinated maintenance instructions, and model drift. The govern-map-measure loop needs one input above all, a trustworthy record of what AI did with what data. An agent is an access path priced in as a control, or paid later as a finding. THE COST OF GETTING THIS WRONG 110 controls CMMC Level 2 when AI tools touch CUI third-party assessed, not self-attested NIST AI RMF GenAI Profile (AI 600-1) prompt injection, hallucinated maintenance, model drift AI tools touching CUI inherit CMMC Level 2 controls, assessed by a C3PAO Govern-map-measure needs one input a trustworthy record of what AI did with what data An agent is an access path. Priced in as a control, or paid as a finding.

AI security for manufacturing: buyer questions

Does DataShield connect directly to PLCs or SCADA?

No. DataShield governs the data plane. It is not an OT protocol gateway. It governs the datasets your agents query: historian extracts, MES records, supplier and engineering data. Those land in governed, tokenized datasets, served over MCP. Control-network segmentation stays with your OT team.

Can agents still link up production data once identifiers are tokenized?

Yes. Tokens are deterministic and join-preserving. The same supplier, part or operator always maps to the same token. So cross-dataset work still holds up. Names, terms, and identities stay out of the agent's context.

How does this help with a customer or CMMC audit?

You hand over evidence, not claims. We record each tool call and tie it to an agent identity, then seal it into a hash chain. A check on that chain tells you if someone altered, added to, cut, or trimmed a log's end. We publish the schema of that report on the architecture page. You can test it yourself.

We run air-gapped or restricted networks. Does this work?

DataShield runs self-hosted, on your own kit, with your keys. There is no SaaS hop in the data path. The details of a given site belong in a quote talk: get an instant quote.

What DataShield retires for manufacturing agents Three mechanisms and the cost each retires: tokenizing CUI and IP at ingest keeps designs out of the agent query path; authorizing every tool call enforces least privilege to the operator interface; a hash-chained audit is the assessed record NIST and CMMC want. It runs self-hosted or in your VPC, with your keys. WHAT THE SPEND RETIRES Tokenize CUI and IP at ingest Designs leave the agent query path Authorize every tool call Least privilege to the operator interface Hash-chained audit The assessed record NIST and CMMC want Self-hosted or in your VPC, with your keys.
Calculate your ROI

Process IP tokenized at ingest. Agent access scoped per call. Audit evidence a prime contractor will take. All of it on your network.

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