AI programs do not create value while they remain trapped in demos, unresolved reviews, or undefined operating boundaries. LSAS Platform brings policy evaluation, scoped authority, runtime controls, and decision evidence into the execution path — so your teams can move forward with clearer rules and a record stakeholders can inspect.
600 organizations worldwide that experienced a breach; Ponemon research sponsored and analyzed by IBM. Overall study: 600 organizations; item-level response count not separately published.
This breach-study population does not represent all organizations.
Not an LSAS performance measurement.
$670,000USD · average difference
The high-Shadow-AI group had $670,000 higher average breach costs than the comparison group.
IBM / Ponemon Institute · 2025 · Independent research
600 organizations worldwide that experienced a breach; Ponemon research sponsored and analyzed by IBM. Shadow AI cohort sizes are not published in the release.
Comparison: High Shadow AI prevalence versus low or no Shadow AI prevalence.
An observed association, not proof of causation.
Not LSAS savings, avoided losses, or a customer result.
Association, not causation. This is not LSAS savings. Shadow AI Intelligence is In Development.
Value with a measurement plan
Establish the baseline. Then measure the difference.
Measured during your pilot. Agree the workflow, denominator, data access and acceptance criteria before drawing conclusions. These are measurement methods, not realized customer outcomes.
01Risk managementAre supported violations and exceptions handled as intended?+
Baseline
Reviewed labeled cases and the current exception process.
Desired improvement
Agree control-specific pass criteria before testing.
Required data
Approved test cases, expected dispositions, actual decisions, receiver observations, review records and exception raised/acknowledged/resolved timestamps.
Method
Matching observed dispositions / applicable labeled cases. For exceptions, measure elapsed acknowledgment and resolution time; report closed-case medians and open-case age separately, alongside misses and disagreements.
Frequency / owner
At baseline, each policy change and pilot close. Security and risk owner.
Acceptance criteria
Reconcile every in-scope case to its policy version; owners accept residual gaps and required retests.
Limitations
A selected test set does not establish general detection accuracy or breaches prevented.
02Operational efficiencyCan reviewers find and reconcile evidence with less effort?+
Baseline
Measured during your pilot: timed evidence-retrieval tasks and reviewer effort.
Desired improvement
Set a task-level target after the baseline is recorded.
Required data
Comparable retrieval tasks, start/end timestamps, reviewer minutes, missing records and reconciliation steps.
Method
Compare median retrieval time and total manual review minutes for matched tasks; retain sample sizes and unresolved requests.
Frequency / owner
Weekly samples and pilot close. Governance operations and audit.
Acceptance criteria
Repeat the same agreed tasks; reviewers verify the retrieved evidence and report effort alongside incomplete results.
Limitations
Differences in task complexity, staffing and learning affect comparisons; no time saving is assumed.
03AccountabilityDoes each decision trace to its effective policy and accountable authority?+
Baseline
Current records with resolvable policy versions, authorization and named owners.
Desired improvement
Agree required fields and the applicable decision population.
Required data
Unique request IDs, effective policy versions, approved ownership context, authorization records and exception dispositions.
Method
Decisions with all required resolvable provenance / applicable unique decisions. Report policy-only linkage separately.
Frequency / owner
Weekly and after authority changes. Policy owner and assurance lead.
Acceptance criteria
Independently reconcile sampled receipts to active authority and policy; retain unknowns and conflicts.
Limitations
A version label alone does not prove approval. Completeness is not compliance or external immutable custody.
04Innovation velocityWhere do governance handoffs delay an in-scope workflow?+
Baseline
Measured during your pilot: review lead time and handoffs for comparable requests.
Desired improvement
Agree desired lead time while preserving mandatory reviews.
Make explicit what an AI application may say, do, access, and release.
Enforce at runtime
Evaluate supported actions against policy before effects or final release.
Produce decision evidence
Record what was checked, which policy applied, and what disposition followed.
AI growth slows where trust becomes a gate.
The barrier is rarely a lack of ideas. It is the point where promising AI must satisfy security, legal, compliance, operations, and executive leadership at the same time. Without an enforceable boundary, every production decision becomes a fresh negotiation.
Governance arrives too late.
Teams discover approval requirements after a workflow is already built.
Policies stay separate from execution.
Written standards describe expectations, but the application still needs a mechanism that evaluates supported actions before they take effect.
Evidence is assembled after the fact.
Stakeholders receive screenshots and explanations instead of a consistent record of governed decisions.
The shift
Move governance from a review event to an operating capability embedded in the workflow.
Govern individual decisions. Understand organization-wide behavior.
LSAS Platform connects runtime policy decisions, organizational governance requirements and inspectable evidence. This foundation lets organizations evaluate common oversight across applications, agents, business units and governed workflows, with accountable owners and a clear path from summaries to investigation.
Governed execution can strengthen the business case for AI.
The value is not a vague promise that governance creates growth. The value is a better path through the practical constraints that keep useful AI from wider adoption.
Advance priority workflows with clearer acceptance criteria.
Define the conditions a workflow must satisfy before wider use, then test those conditions in operation.
Reduce repeated debate across stakeholders.
Give product, risk, security, and legal teams a shared policy and evidence model rather than separate interpretations of the same workflow.
Preserve speed without removing oversight.
Constrain or escalate supported actions when the policy requires it instead of treating every issue as a reason to stop the entire workflow.
Make expansion decisions from observed evidence.
Use governed decision records and pilot findings to decide what is ready to scale, what needs remediation, and what should remain bounded.
Enterprise value
The value of governance is what it enables.
Help people put AI to work, give teams a repeatable operating model and give leadership evidence for where to invest next.
In Development
The network-oriented operational harness, enterprise AI egress integrations, Shadow AI Intelligence and Tokenomics are under development. The views below describe the intended operating model.
Adoption
Give useful AI a path beyond the experiment.
Clear operating boundaries help leaders decide which workflows to expand. Self AI, Team AI and Managed AI describe the intended ownership, controls and evidence for that decision. Profile configuration and contextual reporting are in development.
Accountability
Carry common governance into each workflow.
Reuse organizational policy and evidence while keeping local owners accountable. Teams can build on an agreed foundation instead of recreating the review process for every application.
Decision quality
Make the next decision with a clearer record.
Connect a policy outcome to the application, approved version and context behind it. Give engineering, governance and executive reviewers a shared basis for investigating issues and deciding what to change.
Operational value
Connect visibility to control and cost decisions.
Planned Shadow AI Intelligence, enterprise AI Egress and Tokenomics will support investigation and attributable usage and spend analysis. These operational extensions are in development; benefits require measured coverage and a qualified deployment.
Make value measurable.
Choose the outcome, agree the comparison and use the evidence to make an investment decision.
Positioning is earned through operating discipline.
Organizations increasingly want to be seen as credible AI leaders. That position cannot be claimed through messaging alone. It is earned by showing that innovation and accountability operate together.
LSAS Platform can support that story by helping your organization demonstrate:
Visible executive ownership.
Policies, approvals, exceptions, and escalation paths have accountable owners.
A practical responsible-AI operating model.
Governance is connected to supported workflow decisions, not left only in principles and review documents.
Evidence behind the narrative.
Leadership can point to records of what was evaluated and how policy shaped the outcome.
A repeatable path to wider adoption.
One governed workflow can establish patterns for future teams and use cases.
Credible claim
LSAS Platform can help you demonstrate governance maturity. It does not guarantee public recognition, revenue growth, regulatory approval, certification, or a particular business outcome.
One governed workflow creates value across the organization.
Executive leadership
A clearer basis for deciding where AI can advance, where it must remain bounded, and what evidence supports the decision.
AI & product
Defined runtime constraints that let teams design toward approval criteria instead of discovering them at the end.
Risk, legal & security
A practical way to express supported controls, review exceptions, and inspect how policy affected execution.
Audit & assurance
Decision records showing what was checked, which policy version applied, and what disposition followed.
A smaller first commitment
See LSAS Platform work on your data before you commit to a pilot.
Discovery applies LSAS Platform policy checks to approved data without integrating a live AI workflow. Review findings, coverage gaps, and proposed control points with your stakeholders. Use the evidence to choose the pilot scope and its acceptance criteria.
The offline scanner evaluates content locally and does not upload scan results to LSAS Platform. You control report sharing.
A fixed-scope 90-day paid evaluation gives your organization a bounded way to test LSAS Platform against a real workflow and its operating constraints. The goal is not to stage a governance theater exercise. It is to determine whether the control model, integration path, and evidence are useful in your environment.
Days 0–15
Frame the decision boundary
Select the workflow, accountable owners, policy scope, environments, and acceptance criteria.
Days 16–45
Integrate and exercise controls
Connect the supported workflow, test policy outcomes, and validate escalation and exception handling with operators.
Days 46–90
Evaluate evidence and next scope
Review governed decisions, unresolved gaps, operating requirements, and a recommendation for production rollout or continued qualification.
A disciplined pilot ends with a decision.
At day 90, your team should have a bounded set of observed results: what was integrated, which controls were exercised, what evidence was produced, where gaps remain, and whether a wider deployment is justified. The pilot is the beginning of an operating model — not a substitute for deployment-specific qualification.
Paid pilot program open
Scoped 90-day pilots: Department, Business Unit, Enterprise. Contact for Pricing. Qualifying pilots may receive up to 100% credit toward the first-year platform subscription, subject to conversion and commitment terms.
Department: Includes 10 hours of technical support over the 90-day pilot.
Business Unit: Includes 20 hours of technical support over the 90-day pilot.
Enterprise: Includes 40 hours of technical support over the 90-day pilot.
LSAS Platform findings and decision records describe governance behavior within the supported scope. They are designed to make the decision process inspectable without overstating what the platform establishes.
What LSAS Platform evidence can show
What was checked against policy
Which policy version applied
Whether the supported action was allowed, constrained, escalated, or blocked
The decision record retained by the platform
What it does not establish
Independent factual truth
Clinical correctness or autonomous patient-impact readiness
Every AI environment is different. Work with LSAS Platform through a scoped pilot or design-partner engagement to evaluate governance requirements, deployment architecture, integrations, reporting and operational scale. Agree acceptance criteria with your business, engineering and governance stakeholders.
