AI Governance Consulting and AI Security
Our AI governance consulting sets the rules, access controls, and review steps that let your teams use AI on company data. Then we build those controls into the AI systems.
Same controls as your other systems
- Personal AI accountsCompany licensesIn place
- Unreviewed AI outputHuman reviewIn place
- Agents with broad accessLeast privilegeIn progress
- Rules in promptsNext
- Vendor AI add-onsNext
AUDIT LOG
Your data
Under your control
What Is AI Governance?
AI governance is the set of policies, access controls, and review steps that decide which AI tools people can use, what data those tools can see, and which actions need a person's approval. It includes AI security, which keeps data, prompts, and models from leaking or being misused.
Good governance treats AI like any other business system. You already control who uses email, where files go, and who approves payments. AI tools need the same controls, applied to software that can give different answers to the same question.
Is AI governance a policy or a system?
Both. A written policy says who may use which AI tools and data. The system enforces it with company licenses, role-based access, approval steps, and logs. A policy without enforcement depends on everyone remembering it.
WHEN YOU NEED IT
Signs You Need AI Governance
You need AI governance as soon as people use AI on company data. These are the signs the controls haven't caught up.
People use personal AI accounts
Staff paste company documents into personal chat accounts whose terms may let the provider keep or train on that data.
Agents with more access than they need
An AI agent can read the production data warehouse or delete records, because nobody limited its permissions.
AI in processes that can never fail
AI drafts or makes decisions where a mistake would be critical or irreversible, and nobody checks before it acts.
Decisions you can't explain to auditors
When regulators or auditors ask why the AI did something, nobody can show the inputs or who approved it.
Sensitive data with no rules
PII, health records, or financial data reach AI tools before anyone has decided where that data may be processed or stored.
No view of AI models or cost
Teams pick their own models and tools, and nobody tracks which ones are approved or what each one costs.
OUR APPROACH
How We Govern AI in Production
We govern AI the way you already govern email, with company licenses and access rules. Then we build those rules into the AI systems, so the software enforces them instead of people's memory. Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
Policy on paper
- Personal AI accounts
- Rules written into prompts
- Broad access for every agent
- No record of what the AI did
Controls in the system
Company licenses only
AI runs through your company accounts, under agreements that bar the provider from training on or retaining your data. No personal accounts.
Rules live outside the model
Business rules that can't be broken sit in code the model must call, not in a prompt it might ignore. A car title doesn't release until every document is on file.
Least privilege for every agent
Each agent gets only the permissions its job needs. Analysis runs in a sandbox instead of your production data warehouse, and no agent can delete a database.
A person approves what matters
Where a mistake would be costly or irreversible, the AI proposes and a person decides. Being right 99% of the time isn't enough when the other 1% sells a car without a title.
Do you sell AI governance tools?
No. We don't sell licenses or platforms. Tools such as model guardrails, identity systems, and audit logs help enforce a policy, but they don't write it. We help you set the policy, then build it into your AI systems with tools you already own.
RISK LEVELS
Matching Controls to AI Risk
Not every AI use needs the same controls. A drafting assistant needs less oversight than an agent that changes records, so each use gets the controls its risk calls for.
| RISK LEVEL | TYPICAL USE | CONTROLS |
|---|---|---|
| Low | Drafting, summarizing, and internal search, where a person reads the output before using it | Company licenses, approved models, and access limited to the user's own permissions |
| Medium | Agents that propose actions in business systems, such as resolving a support ticket | Least-privilege access, a person approves each action, and every action is logged |
| High | Costly, regulated, or irreversible decisions, such as releasing a payment or a vehicle title | Hard rules enforced in code, human sign-off, and a full audit trail |
| Security events | Investigating alerts and logs after an alarm | Read-only log access, with the AI proposing a cause for an engineer to confirm |
| Not for AI | Decisions you can't explain to regulators or auditors, or where a wrong action does irreversible damage | Keep AI out of the decision, or limit it to research a person reviews |
How we set the levels
For each AI use we ask what happens if the AI is wrong 1% of the time. If that is unacceptable, the decision gets a hard rule or a person, not a better prompt.
WHAT WE DO
Governance and Security Work We Do
Our AI governance services cover the policy and the engineering that enforces it, for the AI you build and the AI tools your teams already use.
AI governance assessment
We review the AI tools and agents in use, which data each can reach, under whose license, and which actions need approval, then recommend the controls each one needs. It can run inside an AI readiness assessment.
Access and permission design
Role-based access for every agent and AI tool, tied to identity systems you already run. For a national direct lender, the internal loan system uses Azure Active Directory with role-based access control.
Sensitive data handling
Rules for where PII, health, and financial data may be processed and stored. For the same lender, tax documents with PII sit in a dedicated Azure storage account.
Human review and verification
Review steps where mistakes are costly. The lender's AI tax document check produces a report for a human reviewer, not an automated approval.
Audit trails
Logs of what the AI did and who approved it, so you can answer an auditor. For a healthcare support services company, the automation platform keeps a complete audit trail of every automation action.
Application security around AI
AI is only as safe as the application around it. We run security assessments, fix findings such as hardcoded credentials, and add security scanning to CI.
HOW IT WORKS
How an AI Governance Engagement Runs
An AI governance engagement runs in five steps, from reviewing the AI already in use to controls built into your systems.
STEP 1
Review AI use
We review the AI tools and agents in use, where their data is processed, who can access it, and which actions need approval.
STEP 2
Set risk levels
Each use gets a risk level based on what a mistake would cost, from drafting help up to actions that can't be undone.
STEP 3
Agree on the rules
With your team, we set approved tools and models, data rules, approval steps, and how AI cost is tracked.
STEP 4
Build the controls
We build the rules into your systems with company licenses, role-based access, sandboxes, hard rules in code, approvals, and audit logs.
STEP 5
Review and adjust
New AI uses go through the same review, and recurring AI errors feed back into the rules and prompts.
Governing AI tools you didn't build
Vendor AI features in SaaS products need the same review. Some vendors resell model tokens at a markup, and some contracts restrict how you may use AI with their product. We check what each tool can see and on what terms, and recommend whether to keep it, restrict it, or replace it with an integration you own.
What your team gets at handoff
You own everything we build, including the access rules and audit logging. Services we can provide at handoff include knowledge transfer and documentation of each control and where it lives. Ongoing support is also available.
RESULTS
Governance and Security Results
For a healthcare support services company, AI answers pass through a verification agent, and automation runs with human review and a complete audit trail. For a compliance platform provider, a security assessment and remediation addressed 137 vulnerabilities.
HIPAA
Compliant AI for hospital billing reviews
Healthcare
GenAI Hospital Billing Reviews for a Healthcare Solutions Company
Challenge: Medical experts reviewed thousands of billing lines against clinical files by hand, which was slow and costly.
Result: A HIPAA-compliant RAG system cut manual labor hours per patient and lowered operating costs.
Read the hospital billing case studyReal-time
Threat alerts in PagerDuty and Sumo Logic
Fintech
Website and API Security for Piñata
Challenge: Similar platforms were suffering DDoS and credential stuffing attacks that took down websites and APIs.
Result: AWS WAF, AWS Lambda, reCAPTCHA, and real-time alerts in PagerDuty and Sumo Logic make Piñata's apps much harder to disrupt.
Read the Piñata case study"JBS feels more like an extension of our internal team than a traditional vendor relationship."
Senior IT Leader, AmeriVet Partners
SECURITY
Security for AI, Layer by Layer
AI security works in layers, because no single control catches everything. Identity decides who can connect, permissions limit what each agent can reach, rules in code block what must never happen, and logs record what did.
Generative AI security adds risks older systems didn't have, such as prompt injection, made-up answers, and data reaching a provider's training set. The same layers contain them, because an agent can only reach what its role allows and risky actions still need a person.
WHO CAN CONNECT
Identity
Role-based access
Company licenses
WHAT THE AI CAN REACH
AI agent
Only its permissions
Sandbox
Not production
WHAT IT CAN DO
Rules
In code
Approval
A person
Audit log
Every action
COST AND TIMELINE
What Drives AI Governance Cost and Timeline
Cost and timeline depend mostly on how much AI is already in use and how much sensitive data it touches.
- 01AI tools and agents in use
- 02Sensitive data involved
- 03Regulatory and audit requirements
- 04Systems agents can act in
- 05Identity and access systems in place
- 06Approval steps and logging needed
How the timeline is set
Timelines depend on how many AI uses need review and how many controls must be built, and the assessment sets them. Policy work can start right away. Controls can be built in stages, starting where a mistake would cost the most.
How cost is set
We don't quote a number before we understand what's in use. The assessment turns the factors above into an estimate for each control. Many controls use tools you already own, such as your identity system and cloud logging.
ENGAGEMENT MODELS
Ways to Work With Us
Each model stands alone or leads into the next. Compare all the ways to work with us.
Assessment & Strategy
For companies that need clarity before committing to a build.
Start here if you're not sure what to change first. We review the system component by component, weigh difficulty against business impact, and recommend a path for each part, including what to leave alone. You get a roadmap you can act on with us or on your own.
- Technical assessment
- Architecture review
- AI opportunity mapping
- Modernization roadmap
- Prioritized recommendations
Best when: You need to understand the opportunity, risk, architecture, or roadmap before investing in a larger build.
Projects
For companies ready to build or modernize a defined system.
For a defined scope, senior engineers lead delivery from architecture through release. We work in stages, write tests for core components, and ship through CI/CD. You own everything we build, plus knowledge transfer and documentation tailored to your team.
- Modernization projects
- Custom applications
- Cloud platforms
- Data systems
- Integrations
- AI agents
Best when: You have a clear business problem, workflow, or platform that needs to be built, modernized, or connected.
Teaming / Managed Delivery
For companies that need experienced delivery capacity.
When you need more than a single project, services we can provide include architectural guidance for your existing team, hands-on development, QA, and ongoing support. Senior US-based engineers lead our builds, and nearshore support is an option for ongoing work.
- Senior-led teams
- Embedded engineering
- Ongoing development
- Knowledge transfer
- Flexible support
Best when: You need reliable engineering capacity, technical leadership, or ongoing delivery support without adding unnecessary management overhead.
WHY JBS DEV
How to Choose an AI Governance Partner
An enterprise AI security partner should be able to build the controls it recommends. Ask these five questions.
- Do you build the controls, or only write the policy?
- Both. We help set the policy, then our engineers build it into your AI systems with role-based access, sandboxes, hard rules in code, approval steps, and audit logs.
- Have you built governed AI in regulated settings?
- Yes. We've built AI answers with a verification agent for a healthcare support services company, and AI tax document checks with a human reviewer and dedicated PII storage for a lender.
- Will our data stay out of model training?
- Yes. AI runs in your environment, or under your company accounts and enterprise agreements that bar the provider from training on or retaining your data.
- Who decides when the AI is wrong?
- A person, wherever a mistake would be costly. The AI proposes, a person approves or rejects, and the decision is logged.
- Who does the work?
- Senior US-based engineers lead our builds, with nearshore support available for ongoing work.
JBS Dev is a senior-led custom software development company, founded in 1999, that builds, modernizes, and integrates enterprise systems around the technology clients already own.
FAQ
AI Governance Consulting FAQs
What does AI governance consulting include?
It covers reviewing the AI in use, setting risk levels, writing policy for approved tools, models, and data, and building controls such as role-based access, approval steps, and audit logs. Our AI governance consulting services end with controls built into your systems, not a policy document alone.
How is AI governance different from responsible AI consulting?
It usually centers on principles such as fairness and transparency. AI governance puts rules into practice: who may use which tools, what data they can see, which actions need approval, and how decisions are logged. We work on that practical side.
How do you handle AI data security?
We keep data under your control and limit what each AI system can reach. AI runs in your environment or under enterprise agreements that bar providers from training on or retaining your data. Access is role-based, analysis runs in a sandbox, and sensitive data such as tax records with PII can sit in dedicated storage.
What is agentic AI security?
It protects your systems from AI that takes actions. An agent that can call systems needs least-privilege permissions, hard rules in code it can't override, a person's approval for risky actions, and a log of everything it does. No agent should get permission to delete a database.
How do you prevent hallucinations and prompt injection?
We limit what the model decides and what it can touch. Focused agents with small prompts make fewer errors, hard rules are enforced in APIs rather than prompts, and each agent gets only the permissions its role needs. A person approves actions where a mistake would be costly, so a hallucinated or manipulated instruction stops before it does damage.
When shouldn't we use AI at all?
Keep AI out of decisions where a mistake would be critical, where a wrong action does irreversible damage, or where you can't explain the reasoning to regulators and auditors. AI can still research and propose in those cases, with a person making the call.
Can we govern AI features inside our SaaS products?
Partly. You can control who has access, what data the feature can see, and whether it's switched on. You can't change how the vendor's model works, and some contracts restrict AI use. When a vendor feature fails your rules, an integration on your own model accounts gives you control.
How long does it take to put AI governance in place?
It depends on how many AI tools and agents are in use, how much sensitive data they touch, and your audit requirements, and the assessment sets the timeline. Policy work can start right away, and controls are built in stages, starting where a mistake would cost the most.
