AI Security Readiness Summary
Executive Summary
Acme Corp's AI security readiness improved by 6 points this quarter to a composite score of 68/100, placing the organization above the industry average (54) but below the 80 target set by the Board Risk Committee. Gains were driven by org-wide policy attestation and the rollout of role-based training, which lifted training completion to 68%.
Three departments — Finance, Customer Support and Sales — remain elevated risk, and the simulation failure rate (27%) exceeds our 20% benchmark. The two highest-impact threat vectors observed this period are data leakage to public AI tools and deepfake-enabled executive fraud. The 30/60/90-day roadmap on the final page prioritizes containment of both.
Top 5 Organizational AI Risks
- 1Employees pasting customer data into external AI toolsCustomer Support, SalesCritical
- 2Finance team vulnerable to AI-generated invoice fraudFinanceHigh
- 3HR lacks fake candidate / synthetic identity detection trainingHR & RecruitingHigh
- 4Engineering team needs prompt injection awarenessEngineering, AppSecMedium
- 5Executives need deepfake verification protocolExecutive TeamCritical
Most Vulnerable Departments
Simulation Performance
Across 4 simulations run this quarter (AI spear phishing, deepfake voice, vendor invoice manipulation, sensitive data paste), the org-wide failure rate was 27% — above the 20% benchmark. Highest failures concentrated in Finance and HR, consistent with attacker targeting patterns.
Data Leakage Risk
SSO and egress logs show high public-tool usage from departments without enterprise AI licenses. 42 instances of sensitive-pattern input (PII, source code, financial data) were flagged in the last 30 days. A single uncontrolled paste exposes regulated data to a third-party model provider.
Shadow AI Risk
Discovery scan identified 42 unsanctioned AI tools and browser extensions in use across 6 departments. Shadow AI bypasses data governance, logging and retention controls, and is the fastest-growing source of AI incidents — and the hardest to detect after the fact.
Technical AI Security Readiness
Copilot adoption is high across Engineering, but only 38% of engineers have completed the Secure AI Coding track. No prompt-injection tests are in CI, and LLM-backed services ship without a dedicated AppSec review gate. OWASP LLM Top 10 coverage is incomplete.
Risk Distribution
Recommended Actions
- Mandate enterprise AI tooling org-wide; block public LLM endpoints at the egress proxy
- Deploy deepfake verification protocol (out-of-band callback) for all executive comms
- Run quarterly AI phishing simulations with department-level remediation tracking
- Add prompt-injection regression tests to engineering CI for every LLM-backed service
- Tie annual AI policy attestation to performance review and access provisioning
- Establish #ai-incidents response channel with 1-hour SLA and named on-call owner
30 / 60 / 90-Day Remediation Roadmap
- Block public LLM endpoints on managed devices
- Mandate AI policy attestation org-wide
- Deploy ChatGPT Enterprise SSO + audit pipeline
- Launch AI Phishing simulation to all employees
- Roll out role-based modules to Finance, HR, Engineering
- Implement deepfake verification protocol for executives
- Add prompt-injection tests to AppSec CI
- Establish #ai-incidents reporting channel
- Quarterly executive readiness review
- Tabletop exercise: AI-assisted BEC
- Publish internal Safe AI Usage playbook v2
- Re-baseline org readiness score (target ≥ 78)