AI is the hottest lever you can pull right now – in you personal life or to vastly improve your business. I use it daily and I’ve helped founders and teams roll it into marketing, sales, ops, and product.
If you need a really good executive assistant, I’ve also pretty much nailed that (blog post on that coming soon – hit me up if you can’t wait…) as a hobby project.
The hesitation I hear most often isn’t about the value of AI —it’s about risk.
This guide is the playbook to get the speed benefits of AI while keeping crown-jewel data protected and legal headaches at bay.
The Golden Rule: Treat Public AI Like your Smart, Talkative Intern
This is the single most important security data note to drill in:
Never put anything into a public AI tool that you wouldn’t hand to a brand-new intern on day one.
If you wouldn’t give an intern your customer list, HR notes, or financials, don’t paste it into a public chatbot. Depending on product tier and settings, prompts can be logged or used to improve services.
Act accordingly – and definitely do not assume that ticking a box on Chat GPT is any guarantee that your data is safe. As recently as July 2025, shared Chat GPT links were showing up in Google searches…
#facepalm
Practical translation of the intern rule:
- Assume anything you enter could be seen by someone else someday.
- Strip names, emails, and other private info before you share examples or datasets.
- Keep source code, strategic docs, investor info, and detailed financials out of public tools.
That single mindset shift reduces 80% of all avoidable risk.
A Three-Tier Model for Secure AI Adoption
I segment AI usage into three tiers with different risk/benefit profiles. This is how I help teams sequence adoption without slowing down.
Tier 1: Public AI Tools — “Rented Tools”
What it is: General-purpose, public chat or generation tools.
Good for: Low-sensitivity tasks—brainstorming angles, first-drafting generic copy, summarizing public content, learning new concepts.
Main risk: Data confidentiality and retention you don’t control.
Set guardrails with teams:
- Assume public disclosure. Sanitize inputs by default.
- Anonymize aggressively. No PII (Personally Identifiable Information), no internal IDs, no client identifiers.
- Prohibit sensitive inputs. No code, finance, HR, legal strategy, or roadmap leaks.
Investment: Training and discipline, not cash.
ROI: Big productivity bump on safe, low-risk work.
Tier 2: AI-Powered SaaS — “Specialized Software”
What it is: The tools you already pay for, now with AI features (docs, CRM, project tools, cloud suites).
Good for: Context-aware help on your own data—drafting proposals from your templates, summarizing meeting notes, answering internal FAQs, accelerating routine workflows.
Main risk: Vendor practices and infrastructure (how they store, process, and isolate your data).
Run vendor diligence:
- Training & retention: Can you opt out of data being used to train? Is there a zero-data-retention mode on paid/enterprise tiers?
- Certifications & controls: Look for SOC 2 Type II, ISO 27001, GDPR alignment, regional data residency options.
- Admin features: Role-based access, granular permissions, audit logs, SSO, and tenant isolation.
Investment: License upgrades and a few hours of due diligence.
ROI: Team-wide speed on real, company-specific work—without building anything yourself.
Tier 3: Private/Custom AI — “Build Your Engine”
What it is: Your own AI stack—self-hosted models or apps using business-grade APIs with zero-retention.
Good for: Durable, defensible advantage. Think: an internal expert system trained on tickets, playbooks, SOPs, and past deals.
Main risk: Implementation and infrastructure become your responsibility.
How You Should Approach It:
- Use business-tier, zero-retention APIs for any proprietary inputs.
- Consider self-hosting where it truly matters (data never leaves your control).
- Design for isolation: Separate core IP and sensitive datasets from anything AI touches unless you’ve engineered strict boundaries.
Investment: Engineering time, security reviews, infra cost.
ROI: Transformational—proprietary capability competitors can’t simply copy.
A Special Tier: AI for Your Codebase — “The Co-Pilot”
Speed wins. AI coding assistants are a multiplier, but they need adult supervision.
Useful tools: Pair-programming assistants and code-aware chat for boilerplate, unit tests, refactors, and docs.
Risks to manage for teams:
- Security vulnerabilities: Models learn from imperfect public code; insecure patterns can slip in.
- Licensing/IP: Assistants can reproduce copyleft-licensed snippets—bad news if they land in proprietary repos.
- Risky defaults: Auto-generated infra and scripts often ship with overly permissive settings (violating least privilege, exposing ports, etc.).
Set the rules of engagement:
- Mandatory human review. No AI-generated code merges without a senior developer’s eyes.
- Automated scanning. Turn on SAST/DAST and license scanning (e.g., GitHub Advanced Security, Snyk) to catch vulnerabilities and IP issues early.
- Use AI for the boring bits. Tests, boilerplate endpoints, scaffolding… yes. Core business logic and security-critical code… no.
How To Implement This in Practice
Marketing & RevOps (Tier 1 and Tier 2)
- Tier 1: Ideation, outlines, and generic drafts—always with anonymized prompts.
- Tier 2: Proposal writers and content assistants trained on your style guides, offers, and case libraries—kept inside your SaaS with audit trails.
Product & Support (Tier 2 and selective Tier 3)
- Tier 2: Internal copilots that summarize tickets, surface known fixes, and draft responses from your wiki and past resolutions.
- Tier 3 (selective): An internal “expert system” on self-hosted or zero-retention infra for sensitive operational knowledge.
Engineering (Co-Pilot rules + Isolation)
- Co-pilot for velocity: Tests, stubs, repetitive glue code.
- Isolation by design: Core backend and security-critical services live in locked-down repos. AI tools do not touch them.
- Shift-left security: Scanners in CI, mandatory reviews, and sane defaults for infra.
This blended approach gives teams impressive speed where it’s safe, and tight control where it counts.
The Bottom Line: Don’t Fear AI—Manage It
Your competitors are using AI.
You should too. Security isn’t a brake pedal; it’s guardrails that let you go faster without going over the edge. Classify your use cases into tiers, set clear rules for each, and you’ll capture the upside while minimizing the “oh no” moments.
Actionable Checklist (You Can Do This in a 1-2 weeks)
1) Run a 30-Minute AI Kick-Off
- Teach the Intern Rule.
- Define the three tiers and what does/doesn’t go into each.
- Publish a one-pager with examples and prohibited inputs.
2) Standardize Tier 1 Usage
- Create prompt templates that omit PII and sensitive context.
- Add a pre-prompt reminder in shared docs: “No PII / no code / no strategy.”
3) Level-Up to Tier 2 Safely
- Audit current vendors: data retention, training, certifications, admin controls.
- Turn on enterprise settings (SSO, audit logs, RBAC).
- Restrict Tier 2 to data that wouldn’t sink the company if leaked.
4) Govern AI in Engineering
- Enforce human code review on any AI-assisted code.
- Enable SAST/DAST and license scanning in CI.
- Allow assistants for boilerplate and tests; ban them from core IP.
5) Be Honest About Tier 3
- Only build custom/self-hosted AI if you have the engineering + security muscle.
- Isolate sensitive systems. Use zero-retention endpoints.
- Start with a narrow, valuable use case; iterate.
Development Guidelines I Give to Teams
For quick productivity wins
- Use AI to generate small scripts, tests, and repetitive glue code.
- Keep confidential data out of prompts and snippets.
For production systems
- Let devs use AI to debug, document, and scaffold, but require human ownership of core logic.
- Apply the principle of least privilege to any AI-generated infra/config.
- Keep the core backend—the revenue-critical IP—written and reviewed by senior humans.
Reality Check on the Current AI Tooling
The AI race is pushing vendors to ship fast. Some products have rough edges and security gaps. Even on enterprise tiers, be conservative about what data you expose. Assume we’ll see some public cautionary tales.
Proceed with that in mind.
What’s next?
If you want to get started on Tier 1 and 2 and want to chat about it click on my pic to send me a message – I’m big on the nitty gritty of implementation in the daily grind of getting things done in business
If you’re looking for advice on the more serious end of things, like implementation of your own internal AI beast, I’ll refer you to someone who can better help you get a clear picture on where to start.
Click my pic….!
