AI is already in your business. The question is whether you're operating it or just hoping it behaves.
Secure AI is how we design, deploy, and govern the agentic systems running your regulated workflows. We help you build when you're ready, and manage it long-term when performance, compliance, and enterprise scale are non-negotiable.
It’s rarely the model. AI projects fail on integration, governance, and operations.
Every vendor is selling AI. Few can secure, govern, and operate it inside a regulated environment. That's the gap we were built for. Fortellar is not a traditional consulting firm producing advisory decks; we're an operator of AI and agentic systems for organizations that can't afford to get it wrong.
Three services, one operating model: the governance foundation to activate AI safely, the engineering to build the agents your workflows actually need, and the managed service to keep them safe and compliant after they ship. Each stands on its own. Together they're a program you can defend to your board and your auditor on the same day.
Three ways in: activate, build, or run.
Each service is a complete engagement. Most clients start at activation, build once governance is grounded, and move to managed services when agents go to production.
Secure AI Activation
Inventory every AI tool and agent in your environment. Build the governance, data boundaries, and review process that make AI adoption defensible, before the regulator, board, or customer asks.
AI Agent Build
Domain-specific agents, security, cloud, GRC, operations, engineered against your controls from the first line of code. Handed off with a named owner and a runbook, not a black-box demo.
Managed Agent Services
AgentOps as a managed service. 24/7 monitoring, runtime policy enforcement, drift detection, and an audit-ready evidence base, so the agent that shipped stays compliant four quarters from now.
Three moments bring people here.
Each one has a different first step.
Find the sentence that sounds like your situation, and start there.
AI is already in the building and nobody approved it
“Legal asked which AI tools we use. Nobody could answer.”
Shadow AI is an ungoverned data path. Sensitive records leave through tools that were never reviewed, and there's no record of what went where.
A specific workflow is drowning and automation is the answer
“Two analysts spend their week on work an agent should be doing.”
Generic AI shops ship a demo that can't pass review. An agent built outside your controls becomes a finding instead of a saving.
Agents are in production and nobody is watching them
“It shipped six months ago. Is it still doing what we approved?”
Agents drift. Behavior changes as models and data change, and the evidence you showed at launch is already out of date.
AgentOps as a managed service, with an evidence base that stays current.
One operating model across the practice.
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.
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.
Govern
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.
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.
What we govern against and where the AI actually runs.
The standards AI controls are mapped to
AI governance is mapped into the frameworks you already report against, so one control set answers your AI obligations and your existing audits together.
Where we find, build, and watch AI
Coverage reaches the AI already in use and the agents we build, including the tools that arrived without anyone approving them.
The expertise behind this practice.
Data Protection & AI Governance
Classification, ownership, and handling rules written before a model touches the data, so the AI program inherits boundaries instead of inventing them.
See expertiseContinuous Compliance & Evidence
AI controls mapped into the frameworks you already report against, with evidence collected continuously rather than assembled for the AI audit.
See expertiseIdentity & Access Management
Agents are non-human identities with real privilege. They are enrolled, scoped, and reviewed on the same cadence as your people.
See expertiseCloud Security & Governance
Model endpoints, vector stores, and orchestration run on the same guardrails as the rest of the platform, so a new agent cannot quietly reopen a closed finding.
See expertiseThe question isn't whether you'll run AI in production. It's whether you'll be able to prove it's safe.
Thirty minutes with a senior partner. We'll map where you are across activation, build, and run, and what to sequence first.