The Trust Stack
How to Engineer Trust into AI & Critical Infrastructure
Trust is the new uptime.
AI already controls critical decisions in defense, healthcare, and infrastructure. But speed without trust is chaos. And chaos doesn’t pass audits.
This article is for the people who get the call when things go sideways: CISOs, compliance leads, and program managers in high-stakes sectors. If you're still asking, "Can we trust what this system is doing?" you need more than hope. You need proof.
The Trust Stack gives you that. It’s a five-part framework for building trust into AI and operational technology (OT) systems from the ground up:
Secure
Control
Comply
Verify
Prove
At Big Data Plumbing, we use this framework to build secure, compliant, and resilient systems. It drives our Modular Trust Stack™, powers the ZeroTrust Ledger™, and integrates directly into clients' infrastructures.
We recently applied the Trust Stack with a global manufacturer facing critical security gaps across IT and OT networks. Within days, we uncovered exposed credentials, uncontrolled remote access points, and critical operational blind spots. Using the Trust Stack, we quickly replaced scattered fixes with a layered, structured defense, delivering a secure supply chain, confident leadership, and timely product delivery.
Through disciplined application of the Trust Stack, this outcome is repeatable. This framework helps identify weak spots and give regulators, customers, and stakeholders confidence that critical systems are trustworthy. Let’s look at each component of the Trust Stack.
Secure: Build a Foundation You Can Trust
Most breaches don’t begin with advanced exploits. They begin with bad assumptions: a forgotten password rotation, a sensor left exposed, or unchecked AI access.
Someone trusted what should have been verified. That trust was exploited.
Over 80% of confirmed breaches involve weak or stolen credentials. These weren’t sophisticated attacks, just poor access control and unmanaged risk.
In the past, OT felt safe because it was isolated. Today, OT’s isolation illusion vanishes the moment you connect to remote sessions or cloud dashboards. To secure your systems, start here:
1. Zero Trust Architecture (ZTA)
Treat internal traffic with the same suspicion as external. Because sooner or later, it will be.
Assume every user, device, and process is untrusted. Inside or outside the firewall, it doesn’t matter. Each access request must be authenticated, authorized, and logged in real time using strong MFA, verified device health, and least-privilege access.
If an AI server starts behaving out of character, ZTA blocks or challenges it. If a technician logs in at 3 a.m. from an unknown laptop, ZTA stops them until verified.
2. Encryption and Segmentation
Encrypt everything. Isolate everything. Assume nothing is safe just because it’s "internal."
Lock down your data in transit and at rest. Then segment everything. IT and OT should not live on the same network. Labs should not have direct paths to production. Every critical zone must be isolated and monitored.
Most attackers don’t charge the front gate. They slip in dressed as janitors and leave with the keys to the kingdom. Segmentation stops that cold and locks the broom closet behind them.
3. Identity, Access, and Device Management
Most attackers log in, not break in. Don’t make it easy.
This is where most organizations fail. Not because they don’t have the tools, but because they let exceptions slide.
Enforce least privilege. Eliminate shared or generic accounts. Switch to hardware tokens and strong, unique passphrases. Monitor every device. Use Privileged Access Management (PAM) to vault credentials, rotate them automatically, and monitor privileged activity.
Security Is the Foundation. If it's weak, the entire structure is compromised. Every other layer of the Trust Stack depends on getting this part right.
Control: Automate Governance
Security locks the doors. Control decides who gets keys, when, and for what purpose.
In high-stakes environments, static governance doesn't cut it. Policies in binders and quarterly reviews won’t stop an AI model from pushing live code at 2AM or a contractor logging into a critical system from an unvetted device.
Without automation, you're either wide open or locked in a compliance straightjacket. Neither is a great look at 2AM.
To govern with precision, apply these tactics:
1. Dynamic Access and Workload Control
Replace blanket permissions with precise, context-aware access. Just-in-time, just-enough access reduces overreach, misconfigurations, and insider risk.
For people, use role-based and attribute-based controls. An engineer might access model training data only during business hours, from a vetted device, and within a defined project scope.
For systems and AI, tie access to specific tasks. An autonomous drone should receive only the permissions needed to complete its mission. Anything outside that scope is flagged or blocked.
2. Policy-as-Code
Stop relying on people to follow rules. Write the rules into your systems.
With tools like Open Policy Agent (OPA) or even smart contracts, you can hardcode policies that execute automatically.
Example: "AI model X cannot send commands to machinery unless it has current certification and two human approvals." If that condition isn’t met, the action is blocked before it happens. This turns governance into a continuous, automatic layer.
3. Real-Time Monitoring and Intervention
If your customers or the media are your alert system, your system already failed. Log every critical change, including sensor tweaks, AI deployments, and configuration edits. Compare each one to expected behavior. When something goes off script, trigger an alert or rollback immediately. Use tamper-evident logs and automated response tools to keep your system clean, fast, and trustworthy.
Comply: Turn Red Tape into a Weapon
When done correctly, compliance gives you an edge. It proves your systems meet the safety, security, and ethical standards required to operate in high-risk spaces. It also builds confidence with customers, investors, and regulators.
The challenge is the complexity. Especially when you're expected to meet overlapping and constantly evolving requirements.
To meet requirements without slowing down, focus on these essentials:
1. Compliance as Code
Your infrastructure should enforce compliance by design, not duct tape.
Translate requirements directly into your systems and workflows. If the FDA expects a traceable audit trail for AI model updates, your infrastructure should generate that evidence automatically. If CMMC requires MFA and encryption, those controls should be built in and logged continuously.
Automated compliance reduces friction and removes human error. You stop relying on checklists and start producing real evidence, in real time.
2. Map to What Already Works
You don’t need to start from scratch. Every layer of the Trust Stack aligns with proven standards:
Secure maps to NIST CSF's Protect function and IEC 62443 segmentation requirements
Control supports NIST AI RMF governance and oversight functions
Verify and Prove match the audit and evidence demands of frameworks like CMMC, HIPAA, and the FDA’s total product lifecycle model
3. Build for Audit Readiness
If you build for audit from day one, you’ll never need to panic when the knock comes. Every action under Secure and Control should leave a clear, durable trail. That includes access logs, config changes, approvals, and enforcement events.
Tools like Big Data Plumbing’s ZeroTrust Ledger™ anchor that trail with tamper-evident records. Every change is timestamped, tied to a verified identity, and cryptographically sealed. When someone asks if you're meeting the standard, you can point to the proof on your dashboard.
Comply not to stay out of trouble, but to build trust that scales.
Verify: Enforce Continuous Validation
In high-stakes environments, you can't afford to guess. Devices must be authentic, data must be clean, and AI must behave as expected.
Verification is the layer that confirms trust is still intact. Use the following three tactics to catch drift, tampering, or failure before they escalate.
1. Tamper-Evident Logging
Start with logs that can’t be erased or edited.
Logs that can be deleted aren't logs. They’re liabilities. You need append-only records that capture every change, every action, and every decision point.
Use ledgers that produce cryptographic, time-stamped records tied to specific users and systems. If someone tries to cover their tracks, it’s visible. If something changes when it shouldn't, it’s recorded.
2. Device, Data, and Model Attestation
Device attestation ensures that only trusted machines run critical code.
Data attestation confirms that inputs have not been manipulated.
Model attestation guarantees that the AI in production is the same one that was tested and approved.
Every component in your environment should be able to answer one question with proof: "Am I the real, untampered version?" If you can’t prove it, you’ve lost control.
3. Continuous Monitoring and Anomaly Detection
Verification done the right way happens in real time, not in a post-mortem or next week’s meeting.
Build systems that flag unusual behavior the moment it happens. Dormant accounts activating at off-hours? Flag it fast. These are events you need to get alerts on and respond to now.
Verification tells you what’s real, what’s functioning as intended, and what’s compromised. Continuous verification gives you what reports and dashboards can’t: real-time confidence when it matters.
Prove: Demonstrate Trust to Earn Trust
You’ve secured your systems, implemented controls, aligned with regulations, and verified performance in real time. Now comes the hard part: proving it.
Your board needs clarity, your customers need assurance, and your regulators need proof. If you can't show it, you don’t have it.
To close the loop and earn trust, start with these:
1. Make Evidence Automatic
Generate real-time dashboards, access logs, change histories, and traceable decisions as part of the workflow. Audit evidence shouldn’t be a scramble. It should be a byproduct of daily operations.
If you're deploying AI, every model change should be documented, timestamped, and linked to its data lineage. You want to show your current status and supporting evidence without prep time.
2. Build Visibility for Stakeholders
Trust grows when stakeholders see clear, timely proof.
Executives and customers should have access to clear summaries that answer essential questions. A simple trust scorecard showing risk status, incident response activity, or system health is often more valuable than a long report.
3. Use Independent Validation Where It Counts
Third-party validation builds credibility. That might be a certification, audit, or external assessment. The documentation signals to regulators, partners, and procurement teams that you operate with discipline and transparency.
Supplement this with a track record of tabletop exercises, incident response drills, and verified dry-run results. These demonstrate that your team is capable under pressure.
Make Trust a Measurable Advantage
At Big Data Plumbing, we operationalize trust. Our Modular Trust Stack™ and ZeroTrust Ledger™ help ensure your AI and OT environments are secure, compliant, and verifiably trustworthy.
If you think your company could use the Trust Stack, ask yourself:
Are your systems secure by design?
Do you enforce real-time access controls?
Does your compliance program produce real-time evidence?
Are your models and data continuously verified?
Can you prove your trustworthiness under pressure?
Use this framework. Share it. Make trust your default.