AI Security
Executive Summary
Board-level overview of AI security frameworks, business risks, and regulatory landscape
Framework Comparison Matrix
| Framework | Focus | Best For | Effort | Mandatory? |
|---|---|---|---|---|
| NIST AI RMF | Risk Management | Enterprises establishing AI governance programs | Medium | Voluntary (US Federal recommended) |
| NIST 800-53 | Security Controls | Organizations needing detailed control implementation | High | Required for US Federal systems |
| OWASP AISVS | Verification | Development teams building/auditing AI systems | Variable (3 levels) | Voluntary |
| MITRE ATLAS | Threat Intelligence | Security teams & red teamers | Reference material | Voluntary |
Business Risk Translation
How technical AI threats translate to business impact
Prompt Injection
Unauthorized actions, data exfiltration
Data breach, regulatory fines, reputational damage
Model Data Poisoning
Compromised model behavior, backdoors
Wrong decisions, liability, competitive disadvantage
Model Theft/Extraction
IP theft, model replication
Loss of competitive advantage, R&D investment loss
Hallucination/Misinformation
Incorrect outputs, fabricated information
Wrong business decisions, legal liability, trust erosion
Privacy Violations
PII exposure, training data leakage
GDPR/CCPA fines, lawsuits, customer churn
Regulatory Landscape
| Regulation | Status | Applicability | Key Requirements | Penalties |
|---|---|---|---|---|
| EU AI Act | In Force (Aug 2024) | AI systems in EU market | Risk classification, transparency, human oversight | Up to 7% global revenue |
| NIST AI RMF | Published (Jan 2023) | US organizations (voluntary) | Govern, Map, Measure, Manage functions | N/A (voluntary) |
| ISO/IEC 42001 | Published (Dec 2023) | Global (certification available) | AI management system, risk assessment | N/A (certification) |
| SEC AI Guidance | Evolving (2024) | US public companies | AI risk disclosure, governance | Securities violations |
| NIST AI 600-1 | Published (2024) | Generative AI systems | GenAI-specific risk management | N/A (guidance) |
Board-Level Talking Points
Q: Why does AI security require special attention?
AI systems introduce novel attack vectors (prompt injection, model poisoning) that traditional security controls don't address. They also make autonomous decisions that can have significant business impact.
Q: What's our regulatory exposure?
The EU AI Act mandates compliance for high-risk AI systems with penalties up to 7% of global revenue. US federal guidance is evolving, and SEC requires AI risk disclosure for public companies.
Q: How do we benchmark our AI security maturity?
OWASP AISVS provides three maturity levels. Most organizations start at Level 1 (baseline) and progress to Level 2 (standard) within 12-18 months. Level 3 is for high-risk/regulated industries.
Q: What's the investment required?
Initial AI security program setup typically requires 0.5-2 FTEs and tooling investment of $50K-200K. Ongoing maintenance is ~10-15% of initial AI development costs.
Q: What's the risk of inaction?
Average AI-related breach costs $4.5M+. Regulatory fines can reach 7% of revenue. Reputational damage from AI failures (hallucinations, bias) can impact stock price and customer trust.
Ready to Assess Your AI Security?
Use our interactive tools to evaluate your organization's AI security maturity