Enable AI at enterprise scale without losing control of your data, code, and identity.
A two-phase program that moves organizations from informal AI usage on personal accounts to governed, monitored, centrally managed enterprise AI access. Productivity assistant hardening, secure AI-assisted SDLC, and AI usage detection built around enterprise risk.
From shadow AI to governed adoption.
Most organizations are already using generative AI. Personal accounts on public AI tools handle code generation, data analysis, content drafting, and internal workflow automation. Productivity assistants are rolled out without a permission hygiene review. Local RAG systems run on developer workstations. AI plugins and browser add-ins arrive through self-service installs. The activity is real, the productivity is real, and the controls almost never are.
Armour Cybersecurity's Secure AI Adoption Program closes that gap. The engagement establishes an AI Governance Committee, defines the AI Acceptable Use Standard, builds a corporate enterprise AI access layer so employees stop using personal accounts, hardens the productivity assistant deployment before broader rollout, embeds AI-generated code into a secure SDLC with maker-checker controls, and implements detection and enforcement against unapproved AI tool usage across endpoints, browsers, and network channels.
The program is structured in two phases. Phase 1 establishes governance, secure access, hardened platforms, secure SDLC, and sandbox enablement. Phase 2 layers AI usage detection and enforcement, productivity assistant hardening, and supporting collaboration platform, endpoint, and data protection controls. An optional workstream covers security testing of agentic AI applications, chatbots, and API-connected AI workflows for prompt injection, excessive agency, and tool misuse.
Shadow AI vs. governed enterprise adoption.
The gap between an organization that uses AI safely and one that ships sensitive data to personal accounts is rarely about restriction. It is about whether a controlled enterprise alternative exists, and whether anyone is watching the channels that still leak.
AI usage outpacing governance, with no visibility into what is leaving the building.
Employees paste source code, customer records, contracts, and engineering files into personal accounts on public AI tools because it makes them productive. Productivity assistants inherit whatever oversharing already exists in shared document repositories. AI-generated code merges into production without secure review. AI browser extensions and office add-ins arrive without IT review. Nobody can answer the basic question of which AI tools the organization actually uses, what data they touch, or whether usage is improving or worsening.
Controlled AI access, hardened platforms, monitored usage, and secure code paths.
An AI Governance Committee owns the policy. A corporate enterprise AI access layer gives employees a sanctioned alternative to personal accounts. The productivity assistant is hardened before broader rollout. AI-generated code flows through a secure SDLC with maker-checker approval. AI usage detection identifies attempted use of unapproved tools and risky data uploads. The board can see what AI is being used, by whom, for what, and how the risk profile is trending.
What the program covers.
Nine workstreams across the two engagement phases, with an optional workstream for security testing of higher-risk AI applications. Each addresses a specific failure mode of unmanaged AI adoption.
AI Governance & Acceptable Use
Standup of the AI Governance Committee, formal AI use case intake and approval process, AI Acceptable Use Standard, data handling expectations, and an approved AI tool registry with recurring governance checkpoints.
Corporate Enterprise AI Access
Target-state architecture for a centrally managed enterprise AI access layer built on the organization's preferred cloud and identity stack. Deployment of approved foundation models as a sanctioned alternative to personal accounts.
AI Platform & Environment Hardening
Hardening of approved AI services, network and DNS restrictions on unapproved platforms, review of AI browser extensions and office productivity add-ins, controls for local RAG infrastructure, and API/MCP connection security review.
Secure AI-Assisted SDLC
Secure workflow for AI-generated code across dev, test, staging, and production. Access separation, repository and branch protection, security scanning integration, and a maker-checker model that prevents direct production deployment.
Secure AI Sandbox Enablement
Sandbox model for AI experimentation using approved devices, virtual desktops, or dedicated workstations. Rules for synthetic data, controlled pathways to production, and approval requirements for agentic AI and code generation use cases.
AI Usage Detection & Enforcement
Visibility into approved and unapproved AI tool usage across endpoints, browsers, and network channels. Policies that allow sanctioned tools and restrict prohibited platforms, with alerts for risky data uploads to AI services.
Productivity Assistant Hardening
Identity and access review supporting the assistant, permission hygiene across collaboration and document repositories, validation of role-based access boundaries, data protection control review, and evaluation of assistant plugins, connectors, and agents.
Collaboration, Endpoint & Data Protection
Conditional access for AI-related users, third-party app consent restrictions, office productivity add-in governance, endpoint controls for AI users, DLP policies for AI-relevant data movement, and logging coverage for AI activity.
AI & Agentic AI Security Testing
Optional security testing for higher-risk AI applications. Covers prompt injection, excessive agency, unauthorized tool use, memory and context leakage, plugin abuse, weak human approval controls, and sensitive data exposure.
Who this program serves.
Built for organizations that recognize AI is already in use and need to bring it under enterprise control before sensitive data, source code, or customer information leaves the building through an unsanctioned channel.
Organizations Rolling Out a Productivity Assistant
Teams preparing an enterprise AI productivity assistant for broader deployment who need permission hygiene, data protection controls, identity guardrails, and responsible use guidance in place before the assistant is opened to the full workforce.
Engineering & Development Teams Using AI Code Tools
Organizations whose developers use public AI tools and coding assistants for code generation, refactoring, and review, where AI-generated code needs a secure SDLC path before it reaches production.
Regulated Industries with Sensitive Data
Finance, healthcare, legal, energy, and government organizations subject to data residency, privacy, and confidentiality obligations where employees pasting data into personal AI accounts creates immediate regulatory exposure.
Boards & Executives Defining AI Risk Appetite
Leadership teams that have approved AI investment and now need a defensible enterprise program that demonstrates governance, monitoring, and the ability to answer regulator and auditor questions about AI use.
A disciplined methodology across six phases.
The engagement runs as a two-phase program, structured into six execution phases. Phase 1 phases establish governance, access, and SDLC foundations. Phase 2 phases layer detection, productivity assistant hardening, and supporting controls. Optional AI security testing engages alongside as scoped.
AI Use Case Discovery & Governance Standup
Workshops to map current AI usage, identify use cases, and define risk categories. Standup of the AI Governance Committee with representation from IT, security, business, development, engineering, legal, and privacy.
Acceptable Use & Tool Registry
Development of the AI Acceptable Use Standard, definition of permitted/restricted/prohibited usage, formal use case intake and approval process, and an approved AI tool and use case registry with review cadence.
Corporate AI Access & Platform Hardening
Architecture and deployment of corporate enterprise AI access on the organization's preferred cloud and identity stack, with approved foundation models available through controlled enterprise access. Hardening of approved AI services, network restrictions, and API/MCP review.
Secure SDLC & Sandbox Enablement
Definition of the secure SDLC workflow for AI-generated code, maker-checker approval model, sandbox model for AI experimentation, controlled pathways from sandbox to production, and security requirements for higher-risk apps.
AI Usage Detection & Assistant Hardening
Implementation of AI usage detection across endpoints, browsers, and network. Productivity assistant hardening covering identity, permission hygiene, data protection controls, plugin governance, and responsible use guidance.
Ongoing Governance, Reporting & Optional AI Testing
Recurring governance checkpoints, periodic AI usage and exception reporting, alignment with incident response, and optional security testing for agentic AI workflows, chatbots, and API-connected AI applications.
What the organization walks away with.
Nine integrated deliverables that together establish a working Secure AI Adoption program. Every artifact is built to support audit evidence, executive reporting, and the day-to-day governance of enterprise AI.
AI Governance Model & Committee Charter
Documented governance structure with roles, decision rights, approval authority, escalation paths, and the operating cadence of the AI Governance Committee.
AI Acceptable Use Standard
Clear, employee-facing standard defining permitted, restricted, and prohibited AI usage with data handling expectations across business, technical, engineering, and development workflows.
Corporate AI Access Architecture
Target-state architecture diagram and design document covering the enterprise AI access layer, identity provider integration, networking, logging, and security controls on the organization's preferred cloud platform.
Enterprise AI Access Layer Deployment
Deployed corporate AI access with approved foundation models available through controlled enterprise channels, with SSO, MFA, conditional access, and role-based access groups configured.
AI Use Case & Tool Registry
Living registry of approved AI platforms, business owners, users, data types, risk levels, and review dates, structured to support governance reporting and audit evidence.
Secure AI-Assisted SDLC Playbook
Documented SDLC workflow for AI-generated code, environment separation, branch protection requirements, approval workflows, and the maker-checker model for production deployment.
AI Usage Detection & Enforcement Configuration
Deployed visibility and enforcement controls for AI tool usage across endpoints, browsers, and network channels, with alerting for risky data uploads integrated into security monitoring.
Productivity Assistant Hardening Report
Findings report covering identity controls, permission hygiene across collaboration and document repositories, data protection controls, assistant plugin governance, and responsible use guidance with remediation roadmap.
AI Security Testing for Agentic Workflows
Optional testing report for higher-risk AI applications covering prompt injection, excessive agency, unauthorized tool use, memory leakage, plugin abuse, and sensitive data exposure with hardening recommendations.
The numbers behind the work.
Founded by military intelligence veterans with senior advisors from PwC, KPMG, Deloitte, EY, and Mandiant. The track record reflects the discipline.
Clients Served
Organizations across finance, healthcare, technology, energy, legal, and government trust Armour Cybersecurity to protect what matters.
Client Retention Rate
Long-term engagements built on consistent quality, predictable delivery, and consulting relationships that compound in value over time.
Industries · Worldwide Reach
Cross-sector experience spanning every major regulated industry, with operations supporting clients across North America, Latin America, and beyond.
Cybersecurity Technology Solutions
A vetted catalogue of technology partnerships and proprietary methods deployed in support of enterprise AI governance, secure AI access, and productivity assistant hardening.
Adopt AI on enterprise terms, not personal accounts.
Schedule a fifteen-minute discovery call to scope the program. Protecting What Matters starts with knowing where your data is going.
Book Discovery CallFrequently asked questions.
Common questions from CIOs, CISOs, and engineering leaders evaluating a Secure AI Adoption engagement.
What does the Secure AI Adoption Program actually do?
How is this different from a vCISO or general security strategy engagement?
Which AI platforms and models does the program support?
What is included in productivity assistant hardening?
Does the program include security testing of agentic AI applications?
What does the AI Governance Committee look like in practice?
How does the program handle AI-generated code in our SDLC?
Bring AI under enterprise control.
Reach out to scope a Secure AI Adoption engagement. Discovery calls are scheduled within two business days.
Talk to Armour Cybersecurity.
Toronto, ON, Canada
Request a discovery call.
Tell us about your current AI usage, cloud environment, and adoption priorities. A senior advisor will respond within two business days.