NextZen Security · Secure AI engineering
Build, contain and deploy enterprise AI safely.
NextZen Security pairs Forward Deployed Engineering teams with a containment engine for AI deployments: code-level permission boundaries and secure agentic workflows, implemented directly inside your repositories. The engine is being productized from controls proven in client engagements.
30-minute scoping call · No obligation
Written scope and fixed-fee proposal within one business day of your scoping call.
Your AI application
Identity · Requests · Data boundaries
Agent orchestration
Tool access · Approval gates · Logging
Retrieval & data
Access boundaries
Tools & actions
Restricted permissions
Engineering deliverables, defined in your scope
- Reviewed code
- Evaluation tests
- Architecture decisions
- Team handover
Your AI prototype works. Is it ready for production?
Turn deployment concerns into engineering tasks, implemented controls and tests your team can repeat.
From prototype to production
Your prototype is ready, but your team needs help implementing deployment safeguards.
Agents with production access
An agent needs production access, but its permissions have not been reviewed.
Evidence for enterprise buyers
An enterprise customer needs evidence of your AI controls before approving the purchase.
Build security into your AI deployment
Hands-on engineering. Independent security assessment.
Get help building the controls your deployment needs, or test the system you already have. Each engagement has defined responsibilities, deliverables and handover.
Forward Deployed AI Engineering
Build AI integrations, permission boundaries and deployment controls with engineers working in your team and repositories. Receive reviewed code, evaluation tests and a documented handover.
Explore AI engineeringAssess & testAI Application Security Assessment and Red Teaming
Test whether your AI application can expose data or act beyond its permissions. Receive reproducible findings, remediation guidance and an agreed retest.
Explore AI security assessmentWhat an AI assessment can uncover Illustrative examples—not client results
Agent permissions
An agent executes database commands embedded in retrieved content.
Separate retrieved content from executable instructions; restrict tool permissions.
Integration boundaries
An MCP integration can read files outside its permitted directory.
Constrain file access and test the boundary.
Approval controls
An agent can send external emails without the approval required by policy.
Implement and test the required approval workflow.
Findings, severity and framework mappings depend on evidence and the agreed assessment scope.
AI delivery and security testing
Forward deployed engineering and product leadership that gets AI features and agents into production, with threat modelling and adversarial testing on what you build.
Explore capabilitiesAI security architecture and technical assurance
Design decisions and technical testing for the cloud, applications and identities your AI depends on.
Explore capabilitiesGovernance, risk and compliance readiness
Security leadership, AI governance and audit readiness, with the risk register, policy direction and evidence your board, customers and auditors ask for.
Explore capabilitiesFrom deployment brief to team handover.
- 01
Discuss the deployment
Review your AI system, business objective, engineering constraints and intended access.
- 02
Agree the scope
Define responsibilities, deliverables, dates and fixed fee in writing before work begins.
- 03
Build and test
Deploy NextZen control modules and verification tests inside your repositories, adapted to your environment, and test expected behaviour and abuse cases.
- 04
Review and hand over
Deliver reviewed code, the deployed control modules, automated verification tests, documented decisions and a team walkthrough.
Serving the United States
AI delivery aligned with your business and customer requirements.
Before we work together.
Can you work with our existing engineering team?+
Yes. Engineers work alongside your team in the agreed repositories and environments, with responsibilities and access boundaries defined before work starts.
What remains with our team after the engagement?+
The deployed control modules and automated verification tests keep running in your environment, alongside the reviewed code, architecture decisions and handover documentation defined in your scope. A team walkthrough covers how to maintain and run them.
How is the engagement scoped?+
The written fixed-fee proposal defines responsibilities, deliverables and dates. Changes are agreed in writing before additional work begins.
How are deployment changes and ongoing protection handled?+
Engineering access, deployment changes and testing boundaries are agreed before work begins. After the initial sprint, the deployed control modules and verification tests remain active in your environment. A continuous verification software tier — automated threat modelling updates, tool-boundary testing and telemetry for your security and compliance teams — is in development and offered to early clients as it matures.
How is our data handled?+
Evidence is held under NDA, encrypted at rest and in transit, restricted to the named engagement team and destroyed on the agreed schedule. Repository and environment access are defined in scope; client evidence is never used to train any model.
Who delivers the work?+
Your proposal names the accountable team, relevant expertise and review responsibilities. The engagement combines agreed scope and dates, senior review, specialist delivery where needed, evidence handling and handover.
What stands between your AI prototype and production?
Thirty minutes, no obligation. Written scope and fixed-fee proposal within one business day of your scoping call.
Discuss your AI deployment