AI360 Secure

Operate AI Securely. Prove Its Value. Scale With Confidence.

Built for midmarket teams who have to answer for what AI is doing in their business, without the headcount to staff that function themselves.

95% of enterprise AI pilots never reach production or deliver profit and loss impact, according to MIT's NANDA Initiative. Richard Ricks sees the same pattern at Silver Tree: the investment gets approved, the pilot launches, and six months later nobody can point to the revenue, cost savings, or risk reduction it was supposed to deliver. AI360 Secure closes that gap by making someone accountable for the outcome every day the AI runs, not by adding another tool to the stack.

Book an Advisory Session

25-40%

Reduction in manual effort across Silver Tree AI360 engagements

<25%

Of enterprise AI investments realize their intended value (Richard Ricks, Silver Tree)

95%

Of enterprise AI pilots never reach production or P&L impact (MIT NANDA Initiative)

Your AI Pilots Are Multiplying Faster Than Anyone Is Governing Them.

A team stands up a copilot pilot in procurement. Another builds an internal chatbot off a weekend project. A third wires an agent into customer data and nobody outside that team knows it exists. When a board member asks what AI is running and who approved it, the honest answer is often nobody fully knows.

Warehouse worker in a hard hat checking a tablet

No Inventory, No Accountability

When a regulator, acquirer, or board member asks what AI is touching your data right now, a team without an inventory has no good answer. That question surfaces during an audit, a deal, or a breach, never on a convenient timeline.

Team reviewing a stalled AI pilot program on a meeting room screen

Pilots Stall Before They Pay Off

95% of enterprise AI pilots never reach production, per MIT's NANDA Initiative. Each one is budget spent and board expectations set, with nothing to show for either. The model rarely fails. The release gates, monitoring, and named owner around it do.

Stressed professional with head in hand at a laptop

Risk Scales With Every New Use Case

Each new copilot or agent is new exposure: a data leak that becomes a disclosure obligation, a cost spike nobody budgeted for, an outage with no owner to call. Every use case added without governance added is a liability the business can't yet size.

The Two Outcomes We Guarantee with Our AI Practice

AI has to pay off and stay controlled at the same time. Treat it only as a cost center to lock down, and the business case never gets built. Treat it only as a growth lever, and the first incident undoes the investment case entirely. AI360 Secure manages both sides of that equation at once.

Value Realization

Prioritizes the highest-value use cases, redesigns the workflow around them, and tracks the KPIs that prove ROI, not just deployment.

Prioritized high-value use cases

Faster time to value

KPI and ROI measurement

Continuous optimization and scale

Silver Tree AI Engine
One AI Operating Core

Aligned Strategy, Unified Execution.

Risk Management

Governs the risks that undermine adoption: data exposure, compliance violations, inaccurate decisions, reputational harm, operational disruption.

Data exposure

Compliance Risk

Poor decisions

Reputational harm

Operational disruptions

A New Pilot Carries Different Risk Than an Agent Already in Production. Your Oversight Should Match.

A commercial copilot adopted last quarter needs different oversight than an agent already in production, and different again from a use case still in the pipeline. AI360 Secure runs all three without forcing everything through the same process.

AI360 Governance Operations

Commercial AI tools, copilots, and internally built solutions

Turns "nobody fully knows" into an inventory, a risk classification, and an approval record your board can actually see.

AI360 Run

AI already in production: copilots, agents, retrieval, and models

Catches the incident before it becomes a headline, a disclosure obligation, or a cost overrun nobody saw coming.

AI360 Accelerate

Teams ready to expand what AI is doing for them

Keeps the investment case honest: expands what is proving its value, retires what is not, instead of letting the backlog grow on momentum alone.

Nothing Reaches Production Without a Reason Your Board Would Sign Off On.

A use case that skips one of these stages is the one that ends up in the 95% that never reach production, or the one that reaches production without anyone able to explain why it is there. AI360 Secure runs every use case through all eight: Discover, Govern, Design, Build, Assure, Operate, Measure, Scale.

Discover

Govern

Design

Build

Assure

Operate

Measure

Scale

AI360
Secure

Silver Tree advisor presenting an AI governance framework to a leadership team

The Controls Underneath AI360 Map to the Frameworks a Board Already Trusts.

When a board or an auditor asks what standard your AI governance is built on, "we have a policy" is not a credible answer anymore. AI360 Secure aligns to NIST's AI Risk Management Framework (Govern, Map, Measure, Manage) and OWASP's GenAI Security Project, and maps to ISO/IEC 42001 where a client's compliance posture requires it. These are reference points, not certifications. Silver Tree states what it aligns to and does not claim what it has not earned.

The Choices Other AI Vendors Make You Pick Between. Silver Tree Does Not.

We Already Know the Environment

Platform-Neutral by Design

One Team Owns Strategy, Governance, and Operations

Engineer reviewing cloud and analytics dashboards across two monitors

We Already Know the Environment

A vendor starting from zero spends your first quarter on discovery, billed at your expense. Years running data strategy, analytics, DevOps, and managed cloud for the same clients means AI360 starts already knowing the environment.

Engineer assembling hardware at a workbench in an engineering lab

Platform-Neutral by Design

Running five AI tools from five vendors should not mean five governance models to maintain. Microsoft Copilot, Azure AI, OpenAI, Anthropic, AWS, Snowflake, and ServiceNow all run under the same one.

Team lead presenting team commitments and accountability on a whiteboard

One Team Owns Strategy, Governance, and Operations

A strategy team that leaves after the roadmap and an operations team that inherits a system it did not design is how accountability disappears. The practice that advised on the use case is the same one monitoring it in production.

The Numbers AI360 Secure Puts in Front of Your Board, Every Quarter.

A governance program that cannot produce numbers stops being an operating model and turns into paperwork. AI360 Secure reports on the same cadence as the rest of IT operations, not once a year when an auditor asks.

Percentage of AI use cases inventoried and risk‑classified

Time from use‑case intake to approval

Production AI availability and incident rate

Adoption and active‑user growth by use case

Benefits realized against the original business case

The Same Practice Running Your Governance Has Already Found the Money AI Was Leaving on the Table.

AI360 Secure runs on the same team that has already assessed and prioritized AI use cases for midmarket engineering and architecture firms. These three are drawn from that broader advisory work, anonymized at the client's request. None are AI360 Secure engagements specifically, and the page should not imply otherwise. They show how Silver Tree sizes an AI opportunity before recommending anything.

View All Case Studies

Hand marking up engineering drawings with a pencil

Engineering Drawing Search

Mid-market engineering solutions firm

AI-powered visual search cut average drawing search time from 15 minutes to 5. Silver Tree's assessment projected a six-figure annual gain in recovered engineering capacity, plus savings from eliminated drawing rework.

Architects reviewing a physical building model

Spec-vs-Drawing QA/QC

Mid-market architecture and design firm

Of four AI use cases assessed, an automated spec-to-drawing cross-check ranked highest priority, ahead of a BI dashboard and a knowledge-management tool, because undetected errors carried direct legal and financial exposure.

Engineer reviewing CAD models and technical drawings on two monitors

Expertise Capture

Mid-market engineering solutions firm

An AI tool aimed at reducing simulation iterations and expert-review dependency was projected to save 20 to 30% of time against a baseline of roughly 1,400 to 1,600 billable hours per engineer per year.