CCPA / CPRA Compliance

The California Consumer Privacy Act (CCPA) is the most comprehensive state-level privacy law in the United States. The California Privacy Rights Act (CPRA), which amended and expanded CCPA effective January 2023, added new consumer rights and created the California Privacy Protection Agency (CPPA) for enforcement. When your AI application processes personal information of California residents, CCPA/CPRA applies — and sending that data to third-party AI providers like OpenAI or Anthropic creates significant legal risk. Blindfold solves this by tokenizing personal information in the US region before it reaches any AI provider. The LLM only sees anonymized tokens like <Person_1> — never real names, SSNs, or email addresses.

Who Must Comply

CCPA/CPRA applies to any for-profit business that collects California consumers’ personal information and meets any one of these thresholds:

Annual gross revenue over $25 million
Buy, sell, or share the personal information of 100,000 or more consumers, households, or devices
Derive 50% or more of annual revenue from selling or sharing consumers’ personal information

CCPA applies based on where your users live, not where your company is located. If you have California customers, CCPA likely applies to you.

Key CCPA/CPRA Rights

Right to Know (Art. 1798.100)

Right: Consumers can request what personal information is collected, used, disclosed, or sold about them.Risk with AI: If you send consumer data to AI providers, you must disclose this in your privacy policy — and be able to tell consumers exactly what data was shared.With Blindfold: Since only anonymized tokens reach the AI provider, no real personal information is disclosed. Audit logs document exactly what entity types were detected and protected.

Right to Delete (Art. 1798.105)

Right: Consumers can request deletion of their personal information.Risk with AI: Data sent to AI providers may be retained in their logs, caches, or training data — making deletion impossible.With Blindfold: No real personal information reaches the AI provider. For your own records, use tokenize() with mapping deletion — once the mapping is destroyed, tokens become meaningless and irrecoverable.

Right to Correct (Art. 1798.106, CPRA)

Right: Consumers can request correction of inaccurate personal information held by a business.Risk with AI: If inaccurate data is sent to AI providers, corrections cannot propagate to third-party systems.With Blindfold: Real personal information stays in your system where you control it. Corrections only need to happen in your database — the AI provider never had the real data.

Right to Limit Use of Sensitive PI (Art. 1798.121, CPRA)

Right: Consumers can limit the use and disclosure of sensitive personal information — including Social Security numbers, financial account information, precise geolocation, racial or ethnic origin, health data, and biometric information.Risk with AI: Sensitive PI sent to AI providers violates this right if the consumer has opted to limit its use.With Blindfold: Sensitive PI is detected and tokenized before AI calls. The strict policy catches SSNs, financial data, health information, and other sensitive categories automatically.

How AI Creates CCPA Risk

Under CCPA/CPRA, “sharing” means disclosing personal information to a third party for cross-context behavioral advertising or other purposes — and the definition is broad. When you send consumer data to third-party AI providers like OpenAI or Anthropic, this may constitute “sharing” or even “selling” personal information under CCPA. This creates three problems:

Opt-out obligations — consumers who opt out of sharing must have their data excluded from AI provider calls
Disclosure requirements — you must list AI providers as recipients in your privacy policy
Right to delete — data sent to AI providers may be irrecoverable

Tokenization eliminates all three risks. When you tokenize before AI calls, no personal information reaches the AI provider. There is nothing to opt out of, nothing to disclose, and nothing to delete.

Consumer Message                     Blindfold US Region              AI Provider
"Hi, I'm Sarah Johnson,             "Hi, I'm <Person_1>,             AI sees only
 sarah.johnson@example.com,   →      <Email Address_1>,          →   anonymized tokens
 SSN 123-45-6789"                     <US SSN_1>"

                                                                         ↓

"Dear Sarah Johnson,          ←    Detokenize with mapping     ←   "Dear <Person_1>,
 we've updated your..."              (PI stays in US)                we've updated your..."

CCPA Categories and Blindfold

CCPA defines specific categories of personal information (Cal. Civ. Code 1798.140(v)). Here is how Blindfold’s entity detection maps to them:

CCPA Category	Examples	Blindfold Entity Types
Identifiers	Name, SSN, email, address	Person, Email Address, US SSN, Address
Financial Information	Bank account, credit card	Credit Card Number, Bank Account Number, IBAN
Commercial Information	Purchase records, transactions	Handled by custom policies
Internet Activity	IP addresses, browsing history	IP Address
Geolocation	Physical location, GPS	Location, Address
Professional Information	Employer, job title	Organization
Sensitive PI (CPRA)	SSN, financial accounts, health data, biometrics	US SSN, Medical Record Number, Biometric Data

Code Examples

Tokenize Before AI Calls

The most common pattern: protect California consumer data before any AI API call.

from blindfold import Blindfold
from openai import OpenAI

blindfold = Blindfold(api_key="your-key", region="us")
openai_client = OpenAI(api_key="your-openai-key")

consumer_message = (
    "Hi, my name is Sarah Johnson and I need help with my account. "
    "My email is sarah.johnson@example.com, SSN 123-45-6789. "
    "I live at 742 Evergreen Terrace, Los Angeles, CA 90001."
)

# Step 1: Tokenize PI before sending to AI provider
tokenized = blindfold.tokenize(consumer_message, policy="strict")
# → "Hi, my name is <Person_1> and I need help with my account.
#    My email is <Email Address_1>, <US SSN_1>.
#    I live at <Address_1>."

# Step 2: Send only tokens to OpenAI — no "sharing" of PI
completion = openai_client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a customer support agent."},
        {"role": "user", "content": tokenized.text},
    ],
)
ai_response = completion.choices[0].message.content

# Step 3: Restore real values for the consumer
restored = blindfold.detokenize(ai_response, tokenized.mapping)
print(restored.text)

import { Blindfold } from '@blindfold/sdk';
import OpenAI from 'openai';

const blindfold = new Blindfold({ apiKey: 'your-key', region: 'us' });
const openai = new OpenAI({ apiKey: 'your-openai-key' });

const consumerMessage =
  'Hi, my name is Sarah Johnson and I need help with my account. ' +
  'My email is sarah.johnson@example.com, SSN 123-45-6789. ' +
  'I live at 742 Evergreen Terrace, Los Angeles, CA 90001.';

// Step 1: Tokenize PI before sending to AI provider
const tokenized = await blindfold.tokenize(consumerMessage, {
  policy: 'strict',
});

// Step 2: Send only tokens to OpenAI — no "sharing" of PI
const completion = await openai.chat.completions.create({
  model: 'gpt-4o-mini',
  messages: [
    { role: 'system', content: 'You are a customer support agent.' },
    { role: 'user', content: tokenized.text },
  ],
});
const aiResponse = completion.choices[0].message.content;

// Step 3: Restore real values for the consumer
const restored = await blindfold.detokenize(aiResponse, tokenized.mapping);
console.log(restored.text);

Redact Consumer Data from Logs

Permanently remove personal information from application logs to minimize data retention:

from blindfold import Blindfold

blindfold = Blindfold(api_key="your-key", region="us")

log_entries = [
    "2026-02-19 User Sarah Johnson (sarah.johnson@example.com) submitted a support ticket.",
    "2026-02-19 Payment processed for card ending 3456, customer Mike Chen, IP 192.168.1.42.",
    "2026-02-19 Account update requested by Lisa Park, SSN 987-65-4321.",
]

# Redact PI from all log entries (irreversible)
batch = blindfold.redact_batch(log_entries, policy="strict")

for i, result in enumerate(batch.results):
    print(f"Log {i+1}: {result['text']}")
    # Log 1: "2026-02-19 User [REDACTED] ([REDACTED]) submitted a support ticket."
    # Log 2: "2026-02-19 Payment processed for card ending [REDACTED], customer [REDACTED], IP [REDACTED]."
    # Log 3: "2026-02-19 Account update requested by [REDACTED], [REDACTED]."

import { Blindfold } from '@blindfold/sdk';

const blindfold = new Blindfold({ apiKey: 'your-key', region: 'us' });

const logEntries = [
  '2026-02-19 User Sarah Johnson (sarah.johnson@example.com) submitted a support ticket.',
  '2026-02-19 Payment processed for card ending 3456, customer Mike Chen, IP 192.168.1.42.',
  '2026-02-19 Account update requested by Lisa Park, SSN 987-65-4321.',
];

// Redact PI from all log entries (irreversible)
const batch = await blindfold.redactBatch(logEntries, { policy: 'strict' });

for (const [i, result] of batch.results.entries()) {
  console.log(`Log ${i + 1}: ${result.text}`);
}

Right to Delete Implementation

Tokenization naturally supports CCPA’s Right to Delete. When a consumer requests deletion, destroy the token mapping — the tokens become meaningless:

from blindfold import Blindfold

blindfold = Blindfold(api_key="your-key", region="us")

# Original consumer interaction (stored with tokenized text + mapping)
consumer_message = (
    "My name is Sarah Johnson, email sarah.johnson@example.com. "
    "Please cancel my subscription."
)

tokenized = blindfold.tokenize(consumer_message, policy="strict")
# Stored text: "My name is <Person_1>, email <Email Address_1>.
#               Please cancel my subscription."
# Stored mapping: {"<Person_1>": "Sarah Johnson", "<Email Address_1>": "sarah.johnson@example.com"}

# --- Consumer requests deletion under CCPA Art. 1798.105 ---

# Step 1: Delete the mapping from your database
delete_token_mapping(consumer_id="sarah-johnson-123")

# Step 2: The tokenized text is now permanently de-identified
# "<Person_1>" can never be linked back to "Sarah Johnson"
# No real PI remains — deletion obligation satisfied

# Optional: Redact stored records for extra safety
records = fetch_consumer_records(consumer_id="sarah-johnson-123")
for record in records:
    redacted = blindfold.redact(record.content, policy="strict")
    update_record(record.id, redacted.text)

Blindfold as a CCPA Safeguard

Using Blindfold tokenization before AI provider calls provides three key CCPA protections:

No “sale” or “sharing” — the AI provider never receives real personal information, so sending tokenized data does not constitute a sale or sharing under CCPA/CPRA
Data minimization — only anonymized tokens leave your system, minimizing the personal information exposed to third parties
Audit trail — every PI detection is logged with entity types, counts, timestamps, and policy used, providing documentation for CCPA compliance reviews and consumer requests

CCPA/CPRA Compliance Checklist

Identify if CCPA applies to your business

Check if you meet any of the three thresholds: $25M revenue, 100K+ consumers’ data, or 50%+ revenue from selling/sharing PI.

Classify the personal information your AI processes

Map the CCPA categories (identifiers, financial, geolocation, etc.) to the data flowing through your AI application.

Apply Blindfold tokenization before all AI provider calls

Use blindfold.tokenize() with policy="strict" and region="us" to protect consumer PI before it reaches any third-party AI provider.

Implement opt-out mechanisms for data sharing

Provide a “Do Not Sell or Share My Personal Information” link. With Blindfold tokenization, no real PI is shared — but the mechanism is still required.

Enable audit logging for PI processing records

Use Blindfold’s audit trail to document what personal information was detected and how it was protected. Export logs from the dashboard.

Document your data protection practices in your privacy policy

Disclose how you use AI providers, what categories of PI are collected, and how Blindfold tokenization prevents sharing of real consumer data.

Review and update quarterly

CCPA regulations evolve through CPPA rulemaking. Review your compliance posture, privacy policy, and Blindfold configuration at least quarterly.

Getting Started

AI Integrations

Compliance

Essentials

Privacy Methods

SDKs & Tools

Guides

CCPA / CPRA Compliance

Who Must Comply

Key CCPA/CPRA Rights

How AI Creates CCPA Risk

CCPA Categories and Blindfold

Code Examples

Tokenize Before AI Calls

Redact Consumer Data from Logs

Right to Delete Implementation

Blindfold as a CCPA Safeguard

CCPA/CPRA Compliance Checklist

​Who Must Comply

​Key CCPA/CPRA Rights

​How AI Creates CCPA Risk

​CCPA Categories and Blindfold

​Code Examples

​Tokenize Before AI Calls

​Redact Consumer Data from Logs

​Right to Delete Implementation

​Blindfold as a CCPA Safeguard

​CCPA/CPRA Compliance Checklist

Who Must Comply

Key CCPA/CPRA Rights

How AI Creates CCPA Risk

CCPA Categories and Blindfold

Code Examples

Tokenize Before AI Calls

Redact Consumer Data from Logs

Right to Delete Implementation

Blindfold as a CCPA Safeguard

CCPA/CPRA Compliance Checklist