Services
MAISSP helps organizations build visibility, make defensible decisions, implement proportionate controls, and maintain the process as AI changes.
Organizations lack one grounded view of AI use, ownership, and exposure.
Structured interviews, evidence review, inventory development, risk analysis, and executive prioritization.
Current-state brief, risk register, governance map, and phased roadmap.
Known deployments rarely capture embedded SaaS features, experiments, APIs, or employee use.
Create a repeatable discovery and classification process across business and technical contexts.
AI register, classification model, ownership map, and refresh process.
AI risks span security, privacy, legal, operational, and reputational domains.
Establish contextual risk criteria, review paths, treatment decisions, and evidence expectations.
Risk taxonomy, assessment method, treatment plan, and reporting cadence.
Committees and principles often exist without decision rights or operating rhythm.
Design a proportionate cross-functional model connected to delivery, procurement, and security workflows.
Charter, RACI, intake workflow, decision records, and operating calendar.
Generic policy language can be difficult to apply to real employee and engineering choices.
Translate risk posture into clear acceptable-use rules and implementation standards.
Policy set, standards, exception workflow, and enablement materials.
AI functionality and upstream dependencies change the meaning of conventional vendor review.
Add AI-specific diligence for data use, model dependency, access, resilience, and change management.
Questionnaire overlay, review record, risk findings, and contract consideration brief.
AI applications introduce probabilistic behavior and new data and trust boundaries.
Threat-model the use case, architecture, identities, data paths, and operational controls.
Review report, architecture findings, prioritized controls, and acceptance record.
Agents can combine broad context, tools, credentials, and autonomous actions.
Map authority and blast radius; apply least privilege, approval boundaries, logging, and fail-safe design.
Agent register, access model, control requirements, and review checklist.
Point-in-time work decays as vendors, use cases, and organizational priorities change.
Operate the agreed governance process alongside internal owners—not as software or an outsourced decision-maker.
Recurring reviews, updated registers, decision support, issue tracking, and executive summaries.
Existing incident plans may not address harmful outputs, prompt exposure, model changes, or agent actions.
Define scenarios, ownership, triage, evidence needs, communications, and post-event learning.
AI incident annex, playbooks, escalation matrix, and tabletop exercise.
Our work is delivered through structured professional engagement and managed operating processes. We do not imply a proprietary platform, automated control plane, or replacement for your teams. The objective is to strengthen internal accountability with practical expertise, capacity, and continuity.
Start with clarity
An assessment establishes the baseline for a proportionate service roadmap.