AI-Built Internal Tool Review
AI made internal tools dramatically easier to create. Slack bots, Retool apps, dashboards, scripts, agents, refund and support workflows. Some quietly become operational infrastructure — without owners, logging, access review, or a retirement plan. We find them, classify the risk, and give engineering and security leaders a lightweight review process that doesn't kill velocity.
The Gap
Slack bots, Retool apps, scripts, admin dashboards, support helpers, data workflows, AI agents, spreadsheet automations, MCP-connected tools, customer summarizers, reporting pipelines, refund and dispute workflows.
Some are harmless. Some quietly become part of how the company operates. The risk is not that AI was used to write code. The risk is that a useful tool becomes operational infrastructure without clear ownership, review, logging, access control, maintenance expectations, or retirement criteria.
Existing AppSec, SDLC, and governance processes were not designed for tools built in an afternoon by a business team. That is the gap.
What the Review Covers
Surface the tools, automations, scripts, agents, and dashboards that teams rely on. Owner, builder, users, data touched, business process.
A practical Tier 0–4 model that tells you what needs serious review and what can move fast. Not a bureaucracy where everything is high risk.
Who built it, who maintains it, who responds when it breaks, who owns the risk if it produces a bad output. The map and the gaps.
What data each tool reads and writes, what production systems it touches, where outputs go, whether secrets are stored safely.
What stops working if this tool fails. Which tools are more business-critical than leadership realizes. Where fallbacks exist.
Where AI is used at runtime to summarize, classify, recommend, decide, route, or generate. Where humans review and where they should.
For Tier 2+ tools: can you reconstruct who used it, what input went in, what output came out, and which version was active.
How changes are made and reviewed. Whether prompts and configs are versioned. Whether anyone is checking for duplicates or retirements.
Risk Tiering
A practical model. Heavy review only where it actually matters.
Note summarization, drafting, brainstorming.
None or minimal policy guidance.
Internal Slack bot, spreadsheet automation, meeting summarizer.
Owner, basic data check, basic access review.
Support triage helper, internal admin dashboard, reporting automation.
Owner, logging, access control, failure mode, change process.
Refund recommendation, fraud assistant, payment exception, customer-risk classifier.
Formal intake, AppSec, audit logs, human approval path, monitoring, rollback.
Lending support, claims recommendation, employment screening, compliance enforcement.
Governance, legal/compliance, evidence retention, human oversight, explainability, audit trail.
What You Get
A structured list of reviewed tools, workflows, scripts, dashboards, agents, and automations. Owner, data touched, criticality, AI involvement, current review status.
Each tool classified Tier 0–4 with a short reason. Refund Review Assistant — Tier 3, customer financial impact. SQL Helper Script — Tier 1, no runtime AI.
Prioritized findings leadership can act on. Tools with no owner, customer data without access review, business-critical tools without fallback, duplicate tools across teams.
A lightweight decision tree. Personal productivity? No review. Touches customer data? Data review. Writes to systems? Engineering review. Affects regulated decisions? Compliance.
Practical, prioritized actions with owners and timeframes. Assign owner for Refund Review Assistant. Add usage logs for Compliance Summarizer. Retire duplicate spreadsheet automation.
A short leadership summary. What we found, what matters, what is safe to ignore, what needs action, where current governance works, recommended operating model.
How It Runs
The depth of each phase scales with the size and complexity of your environment.
Interviews with engineering, security, platform, and the business teams using internal tools. Existing inventories, AppSec process, AI usage policies.
Build the tool inventory. Classify data exposure, runtime AI use, customer impact, compliance impact, ownership clarity, current review status.
Identify unowned tools, overprivileged tools, operationally critical tools without fallback, runtime AI without logging, tools outside existing review paths.
Risk-tier model, intake form, review decision tree, ownership requirements, escalation rules, prioritized remediation backlog.
Findings, prioritized risks, recommended review process, 30/60/90-day plan. The version leadership reads.
Positioning
What this is
A practical lifecycle and risk review for internal tools that AI made easier to create.
Operational, engineering-native, and tied to outcomes the business already cares about. The output is a working review process — not a binder.
What this isn't
Who It's For
Know which AI-built internal tools have become real operational dependencies — before they become outages, maintenance traps, or hidden risk.
Separate low-risk AI usage from tools that actually need security review. Less noise, better prioritization, clearer data exposure map.
Identify AI-assisted workflows that affect customer outcomes, regulated processes, or evidence trails. Better audit readiness, clearer human oversight.
Keep useful AI-built tools alive without bureaucracy. Teams keep moving, useful internal tools get legitimized, business owners understand responsibilities.
Engineering and security leaders at companies aggressively adopting AI: send a short note and we'll set up a conversation. Blunt takes welcome.
Or copy hello@lamdis.ai — whichever is easier.