S&P 500 AI Risk Ratings 2026
29 entities rated with an average AI score of 30/100 (CC). Based on public repository analysis of AI tools, dependencies, and security signals. Updated June 2026.
29
Entities Rated
CC
Avg Rating
30
Avg Score
0
With Critical Issues
| # | Entity | Industry | Country | Rating | Score | AI Tools | Critical |
|---|---|---|---|---|---|---|---|
| 1 | ServiceNow (NOW) | software | US | BBB | 73 | 24 | 0 |
| 2 | IBM (IBM) | tech | US | BBB | 73 | 1 | 0 |
| 3 | AMD (AMD) | semiconductors | US | BB | 64 | 2 | 0 |
| 4 | Apple (AAPL) | tech | US | BB | 63 | 1 | 0 |
| 5 | Microsoft (MSFT) | tech | US | BB | 60 | 5 | 0 |
| 6 | Salesforce (CRM) | software | US | BB | 57 | 3 | 0 |
| 7 | NVIDIA (NVDA) | semiconductors | US | BB | 56 | 10 | 0 |
| 8 | Oracle (ORCL) | software | US | B | 54 | 2 | 0 |
| 9 | CrowdStrike (CRWD) | security | US | B | 49 | 2 | 0 |
| 10 | Netflix (NFLX) | media | US | B | 49 | 4 | 0 |
| 11 | Intel (INTC) | semiconductors | US | B | 46 | 8 | 0 |
| 12 | Uber (UBER) | transport | US | CCC | 45 | 3 | 0 |
| 13 | Capital One (COF) | finance | US | CCC | 40 | 1 | 0 |
| 14 | Morgan Stanley (MS) | finance | US | CCC | 40 | 1 | 0 |
| 15 | Intuit (INTU) | fintech | US | CCC | 40 | 1 | 0 |
| 16 | Adobe (ADBE) | software | US | CC | 26 | 3 | 0 |
| 17 | Airbnb (ABNB) | travel | US | CC | 25 | 3 | 0 |
| 18 | PayPal (PYPL) | fintech | US | NR | 0 | 0 | 0 |
| 19 | Tesla (TSLA) | automotive | US | NR | 0 | 0 | 0 |
| 20 | Goldman Sachs (GS) | finance | US | NR | 0 | 0 | 0 |
| 21 | JPMorgan Chase (JPM) | finance | US | NR | 0 | 0 | 0 |
| 22 | Palantir (PLTR) | data_analytics | US | NR | 0 | 0 | 0 |
| 23 | Cisco (CSCO) | networking | US | NR | 0 | 0 | 0 |
| 24 | Block (SQ) | fintech | US | NR | 0 | 0 | 0 |
| 25 | Workday (WDAY) | software | US | NR | 0 | 0 | 0 |
| 26 | Palo Alto Networks (PANW) | security | US | NR | 0 | 0 | 0 |
| 27 | Google (GOOGL) | tech | US | NR | 0 | 0 | 0 |
| 28 | Meta (META) | tech | US | NR | 0 | 0 | 0 |
| 29 | Amazon (AMZN) | tech | US | NR | 0 | 0 | 0 |
Methodology: Based on analysis of public GitHub repositories, published dependencies, and public documentation. Ratings reflect publicly observable AI stack health and may not represent the complete internal technology stack.