STEP 01
Inventory
Map every AI system, model, vendor, owner, and the data each one touches.
AI Governance & Secure Deployment
AI governance and secure deployment for companies running AI in production
Profitec AI builds the control layer around your AI — inventory, risk classification, policy, access controls, audit trails, and human oversight — so leadership, security, and compliance teams can adopt AI across the business with evidence, not hope.
AI governance and secure deployment is the operational control layer that lets a company run AI in production without privacy, security, regulatory, or reputational risk: knowing which AI systems, models, and vendors are in use, classifying each by risk, and putting policy, approval, access control, audit trails, and human oversight around them. Profitec AI builds this layer end to end — AI and vendor inventory, risk classification, an AI use policy, model and vendor governance, secure-deployment controls, and audit-ready documentation — so AI moves from ungoverned experiments to a system leadership, security, and compliance can stand behind.
Where the workflow breaks
Where running AI without governance creates risk
01
AI tools, copilots, and agents spread across teams with no inventory of what runs, on what data, or with what permissions.
02
Sensitive data flows into third-party models and vendors before anyone reviews where it goes or how it is retained.
03
Autonomous agents and automations take actions with no access limits, approval gates, or audit trail.
04
There is no AI use policy, approval flow, or model/vendor standard, so every team improvises.
05
Security questionnaires and due-diligence reviews arrive, and there is no AI governance evidence to answer them.
06
When something goes wrong, no log shows what the AI did, why, or on whose data.
What Profitec builds
What the AI governance layer covers
Profitec AI turns governance into concrete, operational work: a live inventory of your AI and vendors, a risk classification for each system, and the policies, controls, and audit trails that let you deploy AI confidently and prove it.
AI and vendor inventory: which systems, models, and tools run, by whom, on what data
Risk classification per use case, including high-risk and autonomous-action contexts
AI use policy, approval workflow, and a standard for adopting new tools and models
Model and vendor governance: review, due diligence, and data-handling terms
Access controls, secrets handling, and least-privilege for AI systems and agents
Human-in-the-loop approval gates on sensitive or irreversible AI actions
Audit trails and logging of AI decisions, inputs, and data flows
Secure-deployment review: prompt injection, data egress, and output-handling risks
Incident response for AI events, and audit-ready governance documentation
Who it's for
Built for the people accountable when AI goes into production.
Compliance & legal
Map AI use against requirements and produce audit-ready evidence on demand.
Security & IT
Control access, secrets, and data egress; review every agent before it ships.
Leadership & founders
Adopt AI across the business with evidence to show customers and auditors.
Teams answering due-diligence
Have governance proof ready for security questionnaires and procurement.
Pipeline
How the governance layer is built and run
Input
Processing
AI / logic
Human control
Output
Measurement
STEP 02
Risk classification
Score each use case by impact, autonomy, and data sensitivity against a clear rubric.
STEP 03
Policy & standards
Set the AI use policy, approval flow, and the bar for adopting new tools and models.
STEP 04
Secure-deployment review
Check access, secrets, prompt-injection, data egress, and output handling before launch.
STEP 05
Controls & access
Apply least-privilege, approval gates, and human oversight on sensitive actions.
STEP 06
Audit trail & monitoring
Log AI decisions and data flows; monitor for drift, misuse, and new shadow AI.
STEP 07
Review cadence
Re-review new use cases, vendors, and models on a standing schedule.
Integrations
Built around the tools you already run.
Frameworks & standards
NIST AI RMFISO 42001EU AI ActOWASP LLM Top 10SOC 2
AI systems covered
ChatGPTCopilotClaudeGeminiCustom agents
Controls
SSORBACSecretsAudit logs
Documentation
AI inventoryAI policyDPIA/AIIAVendor reviews
Risk domains
HRFinanceHealthAutomated decisions
Jurisdictions
European UnionUnited StatesIsrael
Tooling is illustrative. The automation is designed around the systems you already use, connected through APIs and orchestration layers such as n8n and Make.
What improves
Metrics we measure against a baseline.
AI visibility
/01A live inventory of every AI system, model, vendor, and data flow — instead of shadow AI.
Risk posture
/02Each use case classified, with high-risk and autonomous actions flagged and controlled.
Deployment safety
/03Access, approval, and secure-deployment checks in place before AI ships.
Audit-readiness
/04Decisions, inputs, and data flows logged so an auditor or customer can review them.
Deal velocity
/05Governance evidence ready for security questionnaires and due-diligence reviews.
Policy coverage
/06AI use, vendor, and model standards in force across the business.
Controls
Scope, controls, and what we are not
Profitec AI is an AI-governance consultancy. It delivers the operational layer — AI inventory, risk classification, governance workflows, secure-deployment controls, documentation, and reporting. This is operational consulting, not legal advice.
- Profitec AI delivers the operational governance layer — inventory, risk, policy, controls, audit, and documentation.
- Risk-classification logic is transparent and reviewable, not a black box.
- Human review and approval are built into every governance workflow.
- Secure-deployment review covers access, secrets, prompt injection, data egress, and output handling.
- Framework alignment is by reference to NIST AI RMF, ISO 42001, the EU AI Act, and OWASP — designed with, not certified against.
- Sensitive data and vendor reviews are handled under confidentiality.
Implementation
A controlled path from audit to monitoring.
01
Scope & inventory
Align on AI systems, vendors, owners, and data; build the initial inventory.
02
Risk & gaps
Classify each use case by risk and compare current practice against governance and security requirements.
03
Policy & controls
Build the AI use policy, approval flow, access controls, and audit trail.
04
Secure-deployment review
Review AI systems and agents for access, injection, egress, and output-handling risks before launch.
05
Train, operate & review
Train the team, hand over the framework, and keep it current as AI use grows.
Common questions
What teams ask before we start.
01What is AI governance?
AI governance is the operational control layer around the AI a company uses: knowing which systems, models, and vendors run, classifying each by risk, and putting policy, access controls, approval gates, audit trails, and human oversight around them — so AI can be adopted across the business without privacy, security, or regulatory risk.
02How is this different from AI compliance?
AI compliance is the regulatory-readiness track — mapping AI use against requirements like the EU AI Act and GDPR. AI governance is the broader operating layer that also covers security, model and vendor governance, access control, audit trails, and secure deployment. Compliance answers 'are we allowed to'; governance answers 'can we run this safely and prove it.' We deliver both — see our AI Compliance program for the regulatory track.
03Do you cover autonomous AI agents and automations?
Yes. Agents and automations that take real actions are exactly where governance matters most. We add access limits, approval gates on sensitive or irreversible actions, and audit trails, and we review each agent for prompt-injection, data-egress, and output-handling risk before it goes live.
04Which frameworks do you align to?
The operational work is designed with reference to the NIST AI RMF, ISO 42001, the EU AI Act, and the OWASP LLM Top 10, alongside SOC 2 controls — scoped to where you operate. We build governance that maps to these frameworks; we do not issue certifications.
05What do we actually receive?
A live AI and vendor inventory, a risk classification per use case, an AI use policy and approval workflow, model and vendor governance, secure-deployment controls and review, audit trails, and audit-ready documentation — plus an optional review cadence that keeps all of it current as your AI footprint grows.
Next step
Put a control layer around the AI you already run
A focused review maps your AI systems, vendors, and the actions they take — then shows the governance and secure-deployment controls worth building first, and how to prove them to a customer or auditor.