Your team is already using AI. Nobody can tell you where, with what data, or who approved it.

Why This Matters

AI adoption moved faster than your governance did and the gap is where vulnerabilites live.

Every department wants an AI pilot. Most already have one running... whether it’s a personal account or a model call buried in a third-party workflow, shadow AI is already expanding your attack surface. These invisible endpoints bypass your risk register but remain fully visible to auditors.

We don't slow down your team with manual approvals; we build the governance guardrails that make every prompt safe by design. Our process ensures your team can innovate at full speed within a secure, zero-retention environment that automatically blocks data egress and keeps your proprietary IP where it belongs. We deliver AI enablement that is safe, audit-ready, and moving at the speed of innovation.

40%

of agentic AI projects fail due to poor integration and governance

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By The Numbers

XX%

of enterprises discovered previously unknown AI agents in their environment in the past 12 months

Cloud Security Alliance/ Token Security, April 2026 (418 IT & security professionals)

XX%

of organizations reported confirmed or suspected AI agent security incidents in the last year

Gravitee, State of AI Agent Secuirty 2025 (900+ executives and practioners

If any of these sound like your last conversation with your board, you're in the right place.

Who This Is For

Situation 1

Already deployed, no governance

Copilot, ChatGPT Enterprise, and a few vendor-embedded agents are already live. Nobody can produce a single document that says what data they touch or who signed off.

Situation 2

Regulator, customer, or board started asking

A board member asked about AI risk. A customer sent a vendor questionnaire with 40 AI-governance questions. Your CEO wants a one-pager by Friday.

Situation 3

About to launch something high-stakes

You're about to put a model into claims, clinical, or customer-facing workflow. You need to prove it's safe before it ships, not explain it after an incident.

Best Outcome

A current AI inventory and a governance baseline you can show an auditor.

Best Outcome

A defensible AI program narrative mapped to NIST AI RMF and ISO 42001.

Best Outcome

A use-case review and control plan signed off before go-live.

What you own at the end: not a deck, but artifacts your team can run with.

Every output is something you can point to in a board meeting, hand to an auditor, or operationalize into your program.

What's Included

After this engagement, you will have:

An AI tool inventory and risk register

Every tool, agent, and model in your environment with data classification, ownership, and a risk score mapped to NIST AI RMF.

After this engagement, you will have:

AI governance framework mapped to NIST AI RMF and ISO 42001

Roles, review gates, acceptable-use, and decision rights written for your organization, not a template someone downloaded.

After this engagement, you will have:

Data-handling controls for AI workflows

DLP, prompt/response logging, PHI and PII boundaries, and vendor-processing terms enforced at the tool and network layer.

After this engagement, you will have:

A use-case intake and review process

The form, the reviewers, the SLAs, and the decision log, so the next AI request doesn't land in someone's inbox and die there.

After this engagement, you will have:

A board-ready AI risk narrative

One page your board can read, a comprehensive briefing your audit committee can defend, and a set of metrics you'll actually report against.

After this engagement, you will have:

A prioritized roadmap and investment plan

What to fix this quarter, what to build next, and what to outsource, resourced and sequenced against business value.

One operating model across the organization.

Whether we're activating, building, or running, the approach is the same three-part discipline: secure the foundation, govern the program, and enable the people who will actually use AI day to day.

How It Works

Phase 1

Secure

Cybersecurity Foundation

We harden the foundation underneath every AI initiative: risk assessments and threat modeling, data classification and DLP, identity governance for AI tools, shadow-AI discovery, regulatory mapping (NIST AI RMF, EU AI Act), and incident response built for AI failure modes.

Phase 2

Operate

AI Governance & Policy

We stand up the program that keeps AI accountable — acceptable-use policy, an approved tool catalog with vetting, data-handling standards, an AI governance board, shadow-AI monitoring, and continuous compliance evidence rather than point-in-time attestations.

Phase 3

Enable

Adoption & Upskilling

We make AI usable by the people who actually do the work — role-based literacy training, department-level use case identification, approved sandboxes by function, change-management playbooks, and ROI tracking that proves the program is working.

You Walk Away With

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Best Outcome

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Best Outcome

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Expertise This Work Draws On

Drawn from the capabilities underneath Fortellar's full practice.

Cybersecurity & Compliance

Compliance Framework Alignment

NIST AI RMF, ISO 42001, HIPAA, and HITRUST mapped to your AI program, so one set of controls satisfies multiple audits.

Cybersecurity & Compliance

Identity & Access Management

Agent identity, service-account hygiene, and human-in-the-loop approval gates for AI actions that touch regulated data.

Cloud & Technology Infrastructure

Cloud Security Posture Management

The AI workloads live in your cloud. We make sure the surrounding network, secrets, and logging posture holds up to scrutiny.

Technology & Security Operations

Logging & Audit Trail

Every prompt, response, and agent action captured, retained, and queryable, providing the evidence base an auditor will ask for first.

Ready to solidify your AI security foundation?

A 60-minute consultation to help you define the scope of your AI governance gap. We’ll walk through our phased activation process and determine the most critical security and compliance priorities for your team.