FREE RESOURCE | AI ADOPTION CHECKLIST
10 Questions to Ask Before Deploying AI at Work
A security-first checklist for leaders, managers and IT teams who want AI to deliver productive value without creating new risks across data confidentiality, vendor supply chains, access controls, or regulatory compliance.
- Data classification, residency & model training restrictions (Q1 to Q3)
- Access management, shadow AI & adversarial prompt threat-modeling (Q4 to Q6)
- Legal compliance, named accountability, logging & staff training (Q7 to Q10)
- Includes the Complete 10-Point Readiness Scorecard for Pilot Decisions
Free instant access • No credit card required • Standard 8-page printable guide • CyberSilience 2026 Edition
OFFICIAL CHECKLIST
AI Adoption Checklist & Readiness Scorecard
- Document Format: Complete 8-Page Printable PDF
- Target Audience: Leaders, Managers, IT & Privacy Officers
- Core Coverage: Data, Vendors, Access, Legal & Human Controls
- Self-Assessment: 10-Point Readiness Scorecard Included
- Publisher: CyberSilience Inc. & CyberSilience Limited
Rule of thumb: “Any question that is not answered means the deployment is not ready. Resolve it or reduce the scope before going live.”
THE SECURITY-FIRST APPROACH
Why Security Comes First in AI Adoption
Most AI conversations start with what the tool can do. Ours starts with what it can expose. Every AI deployment connects your data, your people, and your systems to a new technology and, usually, a new third-party vendor. Getting the security and governance questions answered first is what lets your organization move faster later with confidence.
Pillar 1: Data & Vendors
If you have not classified your data first, you cannot judge any other risk. Clear boundaries protect your proprietary assets.
- Data classification & boundary restrictions (Q1)
- Data retention, model training & privacy terms (Q2)
- Third-party vendor security review & SOC 2 audit (Q3)
Pillar 2: Access & Attack Paths
Unmanaged accounts and shadow AI put company data into systems security cannot see, monitor, or switch off.
- SSO, MFA, least privilege & shadow AI discovery (Q4)
- Threat-modeling prompt injection & adversarial risk (Q5)
- Permissions, plugin scope & human approval gates (Q6)
Pillar 3: Legal & People
The strongest control is an informed employee, and a named owner must stand behind every AI outcome and decision.
- Privacy regulation, NDAs & intellectual property (Q7)
- Named accountability & human oversight of output (Q8)
- Security logging, kill switch & incident response (Q9)
- Staff training, acceptable use & measurable ROI (Q10)
COLLABORATIVE ADOPTION PROTOCOL
How to Use This Checklist
Effective AI governance is not an IT exercise alone. Deploying AI safely requires multi-disciplinary alignment before running pilots or purchasing licenses:
- 1. Work through each question with the business owner, IT/security, and legal or privacy teams in the same room.
- 2. Score every question on the final page matrix: Ready, Partly, or Not yet.
- 3. Any question that is not answered means the deployment is not ready. Resolve it or reduce the scope.
IN-DEPTH CHECKLIST OVERVIEW
The 10 Questions Inside the Guide
Each question in the checklist equips your evaluation committee with exact questions for your internal team and vendors, paired with immediate red flags to watch for.
QUESTION 01
What data will this AI touch, and how sensitive is it?
Data exposure is the number one way AI deployments go wrong. If you have not classified the data first, you cannot judge any other risk.
- Which data classes (customer, employee, financial, IP, regulated) enter the tool?
- Is any of this data prohibited from leaving our environment today?
- Can we limit the tool to the absolute minimum data it needs?
- RED FLAG: Nobody can list the data sources, or the answer is "everything in the shared drive."
QUESTION 02
Where does our data go, and is it used to train models?
Prompts, files, and outputs may be stored, logged, reviewed by staff, or reused. Terms differ sharply between consumer, business, and enterprise tiers.
- Where is data processed and stored (geography), and for how long?
- Is our data contractually excluded from model training, not just a setting?
- Can we delete our data on request and receive formal confirmation?
- RED FLAG: Retention or training terms are vague, or they change with an unchecked plan tier.
QUESTION 03
Has the vendor passed a real security and risk review?
An AI vendor becomes part of your supply chain and your attack surface the day you connect it.
- Do they hold independent assurance (SOC 2 Type II, ISO 27001)?
- What is their breach notification commitment, and who are their sub-processors?
- What is our exit plan if they change terms, get breached, or shut down?
- RED FLAG: The tool was adopted with a credit card and a click-through agreement.
QUESTION 04
Who gets access, and how is that access controlled?
Unmanaged accounts and “shadow AI” put company data into tools that security cannot see or switch off.
- Is access through single sign-on with MFA, tied to joiner/mover/leaver processes?
- Are roles least-privilege, with administrative rights tightly held?
- Do we have a systematic way to discover unsanctioned AI tools in use?
- RED FLAG: Shared logins, personal accounts used for work, or no list of who is using what.
QUESTION 05
How could this system be attacked or manipulated?
AI introduces new attack vectors: prompt injection, output data leakage, poisoned content, and manipulated AI messages.
- Has anyone threat-modelled injection from emails, documents, and web pages?
- Have we tested it adversarially before go-live, and will we retest after updates?
- Could it be leveraged by attackers for convincing phishing or impersonation?
- RED FLAG: The only testing done was casually checking that it gives good answers.
QUESTION 06
What can the AI actually do, not just say?
Once an assistant can send email, edit records, run code or call APIs, a bad output becomes an unauthorized bad action.
- Which connectors, plug-ins, and permissions does it hold (read-only where possible)?
- Which actions require a human to approve before they execute?
- Can administrative teams revoke its system access instantly?
- RED FLAG: Broad write access granted "to make it more useful" with no human approval gate.
QUESTION 07
Which laws, contracts and policies apply?
Privacy law, sector regulation, client confidentiality, copyright, and emerging AI rules all follow the data and the decision, not the tool.
- Have legal, privacy, and compliance reviewed the use case and impact assessment?
- Do client contracts or NDAs restrict use of third-party AI on their data?
- Who owns the outputs, and are there intellectual property or licensing risks?
- RED FLAG: Compliance and legal are asked to sign off after the pilot is already live.
QUESTION 08
Who is accountable when the AI is wrong?
AI outputs can be confident, inaccurate, and biased. Someone named must own the decision and the operational outcome.
- Who is the named business owner for this system, and who is the risk owner?
- Where is human review required, and what specifically does the reviewer check?
- How are errors, hallucinations, bias, and complaints reported and corrected?
- RED FLAG: "The AI said so" is treated as a sufficient explanation for a business error.
QUESTION 09
Can we monitor it, log it and respond if it goes wrong?
You cannot investigate what you did not record. AI needs to be inside your existing detection and incident response, not beside it.
- Are prompts, outputs, and admin actions logged and fed to security monitoring?
- Does incident response cover an AI data leak with a tested kill switch?
- Who reviews usage anomalies, prompt drift, and system behavior, and how often?
- RED FLAG: No logs, no designated owner for alerts, and no incident plan for AI scenarios.
QUESTION 10
Are our people trained, and is the value worth the risk?
The strongest control is an informed employee, and a deployment without a clear business case is risk with no return.
- Is there an acceptable use policy in plain language, and has everyone been trained?
- Do staff know what never goes into an AI tool and how to report a concern?
- What measurable outcome justifies the risk, and when will we review it?
- RED FLAG: A rollout announced by company email with no training and no success measure.
SELF-ASSESSMENT FRAMEWORK
The AI Readiness Scorecard
Mark each question honestly on the 3-state matrix: Ready, Partly, or Not yet. Critical Rule: One “Not yet” in questions 1 to 6 is an immediate stop sign.
8 to 10 Ready
PROCEED WITH CONTROLLED PILOT
Your organization has addressed critical data, vendor, and access controls. You are in a strong position to launch a controlled pilot.
- Proceed with a phased pilot with defined user groups
- Maintain continuous prompt logging & security monitoring
- Schedule periodic reviews for model drift and policy updates
5 to 7 Ready
CLOSE GAPS BEFORE EXPANDING
Identified gaps present real security, privacy, or vendor exposure. Do not scale until high-priority questions are resolved.
- Close foundational gaps starting with data, vendor, and access
- Exclude sensitive customer or proprietary data until resolved
- Draft acceptable use guidelines and establish incident points
Under 5 Ready
PAUSE AND FIX FOUNDATIONS
The deployment carries unmanaged risk. Proceeding creates high risk of data breaches, non-compliance, or brand damage.
- Pause deployment immediately to prevent accidental leaks
- Reduce project scope or test in isolated sandbox environments
- Engage CyberSilience to build core governance and risk foundations
GET YOUR FREE COPY
Download the Complete Checklist & Scorecard
Download the full 8-page PDF document now to evaluate current tools, run team security reviews, and make informed, confident AI decisions.
- Instant PDF access with direct download link
- Printable A4 / Letter format for leadership meetings
- General guidance for risk awareness and productive discussion
NEED HELP IMPLEMENTING SAFE AI?
Practical AI Governance & Risk Training
CyberSilience trains teams and leaders to adopt AI safely, with practical security awareness, governance, and risk training built for real workplaces. Whether you need executive alignment or workforce training, we help you deploy safely.
AI for Leaders
A focused session for executives, senior leaders, and boards to clarify AI opportunities, legal obligations, and strategic governance priorities.
- Identify strategic opportunities & operational limits
- Define board-level oversight and risk accountability
- Establish corporate AI governance frameworks
Secure AI at Work
Empower employees to safely use AI tools in everyday work without exposing sensitive company data, customer details, or proprietary code.
- Safe prompting habits & data classification awareness
- Recognizing AI hallucination & verification techniques
- Clear guidance on acceptable vs prohibited AI usage
AI Readiness Assessment
Independent expert evaluation of planned AI pilots, vendor security architectures, API data flows, and internal acceptable-use policies.
- Detailed vendor security & third-party risk review
- Adversarial threat modeling for AI workflows
- Custom readiness report with gap remediation roadmap
