Crime & Justice Website Classification API Privacy Considerations

You need to automatically detect and act on Crime & Justice-related websites in your pipeline—for example, blocking certain categories for brand safety, flagging high-risk signups, or auditing where your ads appear. By the end of this guide, you’ll have a working request to the categorize endpoint, production-ready code to convert the response into allow/deny decisions, and an operational checklist to run this at scale while respecting user privacy.

Why Klazify fits Crime & Justice classification work

Teams who monitor or filter Crime & Justice content typically face edge cases: court transcripts hosted on news subdomains, advocacy pages that co-mingle with fundraising, or legal resources translated across multiple languages. Klazify is designed for these nuanced cases:

Illustration: Crime & Justice Website Classification API Privacy Considerations

Accurate website categorization using AI: The API analyzes on-page content (not just metadata), which is critical when a Crime & Justice page lives under a broader domain (e.g., a news site’s justice section).
Global coverage: Crime & Justice content frequently spans jurisdictions and languages; Klazify’s analysis helps keep categorizations consistent across locales.
Real-time classification: Ideal when you need fresh context on newly published cases, briefs, or policy statements.
Industry-level categories via IAB taxonomy: The response includes IAB-aligned signals so you can map to industry workflows for ad safety and content filtering.
Simple API integration: A single REST endpoint returns categories, company context, social links, logo URLs, and domain signals, so you avoid stitching multiple data sources.
Compliance-ready filtering: Brand safety and filtering policies can be implemented cleanly with deterministic rules over the returned categories and confidence.

Throughout this article, we’ll show you how to plug the categorize endpoint into your classifier, cache results, and design a clear allow/deny policy for Crime & Justice sites while staying privacy-aware.

Scenario: From policy to production rule

Let’s make the scenario concrete. Suppose you maintain a brand safety rule to avoid showing ads on pages centered on Crime & Justice topics, or you need to flag signups from sites whose primary content is Crime & Justice so that your compliance team can review. You’ll use Klazify to:

Send a URL or domain to the categorize endpoint.
Read the categories and optional IAB mapping returned.
Convert those categories into yes/no decisions (e.g., block/allow, review/auto-approve).
Store and cache results by domain to minimize repeated lookups and exposure of user data.

We’ll walk through a request, the JSON you’ll parse, and a small Python module that implements your decision logic.

Make a POST request to classify a Crime & Justice URL

The categorize endpoint is used for website classification and content categorization. You send a URL in the body of a POST request and authenticate with a Bearer token.

curl -X POST "https://www.klazify.com/api/categorize" \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
--data '{ "url": "https://www.justice.gov" }'

Notes:

Use HTTPS and Bearer auth. Replace YOUR_API_KEY with your actual key.
Place the canonical URL you want to evaluate in the request body. Many teams standardize to domains (e.g., https://www.justice.gov) but page-level classification is also supported.
For operations at scale, log the URL you sent together with a normalized domain (e.g., registrable domain) so you can reuse a cached decision.

For details on parameters and behaviors, see the Documentation. You can start testing by creating a free account from the Register page.

Understand the JSON response and the fields you’ll use

Below is an official example response. Use this structure to wire up your parser and production logic. Keep in mind that your actual categories and confidence scores will depend on the site you classify.


{
"domain": {
"categories": [
{
"confidence": 0.92,
"name": "/Computers & Electronics/Consumer Electronics",
"IAB-632-596": "Consumer Electronics/Technology & Computing/Consumer Electronics"
},
{
"confidence": 0.89,
"name": "/Internet & Telecom/Mobile & Wireless/Mobile Phones"
}
],
"social_media": null,
"logo_url": "https://klazify.s3.amazonaws.com/2110787991611585019600ed5fb1d1300.04730104.png"
},
"success": true,
"objects": {
"company": {
"url": "https://www.apple.com/",
"name": "Apple",
"city": "Cupertino",
"stateCode": "CA",
"countryCode": "US",
"employeesRange": "100K+",
"revenue": 274515000000,
"raised": null,
"tags": [
"E-commerce",
"Consumer Electronics",
"Mobile",
"B2C"
],
"tech": [
"omniture_adobe_analytics",
"atlassian_confluence",
"successfactors",
"apache_apex",
"talend",
"oracle_peoplesoft",
"salesforce",
"stripe",
"dell_boomi_atomsphere",
"gigya",
"sage_50cloud",
"quickbooks",
"webmethods",
"apache_tomcat",
"alteryx",
"tibco_rendezvous",
"atlassian_jira",
"..."
]
}
},
"domain_registration_data": {
"domain_age_date": "1987-02-19",
"domain_age_days_ago": "13026",
"domain_expiration_date": "2030-02-20",
"domain_expiration_days_left": "123"
},
"similar_domains": [
"bestbuy.com",
"icloud.com",
"microsoft.com",
"macrumors.com",
"google.com",
"samsung.com",
"twitter.com",
"hp.com",
"bhphotovideo.com",
"dell.com"
]
}

How to use this structure for Crime & Justice policies:

domain.categories: You’ll base your allow/deny decisions on the name field and optionally the IAB mapping, when present. The confidence helps set thresholds (e.g., block if confidence exceeds a cutoff).
objects.company: Useful for enrichment and auditing; for example, showing a compliance reviewer the organization name and tags; do not use this alone for category decisions.
domain.logo_url: Handy for internal dashboards and audit UIs to quickly recognize the brand under review.
domain_registration_data: Some teams treat recently registered domains differently; you can add a secondary rule (e.g., manual review for very young domains that appear Crime & Justice-related).
similar_domains: Can power prefetching or wider brand-level audits (e.g., check related properties once a primary domain is flagged).

Additional JSON examples to validate your parser

Use the same official structure below in your unit tests to confirm your parser and decision function handle categories arrays, optional IAB mappings, and null social links. These are identical to reinforce correct parsing without relying on unvetted variations.

Sample JSON A


{
"domain": {
"categories": [
{
"confidence": 0.92,
"name": "/Computers & Electronics/Consumer Electronics",
"IAB-632-596": "Consumer Electronics/Technology & Computing/Consumer Electronics"
},
{
"confidence": 0.89,
"name": "/Internet & Telecom/Mobile & Wireless/Mobile Phones"
}
],
"social_media": null,
"logo_url": "https://klazify.s3.amazonaws.com/2110787991611585019600ed5fb1d1300.04730104.png"
},
"success": true,
"objects": {
"company": {
"url": "https://www.apple.com/",
"name": "Apple",
"city": "Cupertino",
"stateCode": "CA",
"countryCode": "US",
"employeesRange": "100K+",
"revenue": 274515000000,
"raised": null,
"tags": [
"E-commerce",
"Consumer Electronics",
"Mobile",
"B2C"
],
"tech": [
"omniture_adobe_analytics",
"atlassian_confluence",
"successfactors",
"apache_apex",
"talend",
"oracle_peoplesoft",
"salesforce",
"stripe",
"dell_boomi_atomsphere",
"gigya",
"sage_50cloud",
"quickbooks",
"webmethods",
"apache_tomcat",
"alteryx",
"tibco_rendezvous",
"atlassian_jira",
"..."
]
}
},
"domain_registration_data": {
"domain_age_date": "1987-02-19",
"domain_age_days_ago": "13026",
"domain_expiration_date": "2030-02-20",
"domain_expiration_days_left": "123"
},
"similar_domains": [
"bestbuy.com",
"icloud.com",
"microsoft.com",
"macrumors.com",
"google.com",
"samsung.com",
"twitter.com",
"hp.com",
"bhphotovideo.com",
"dell.com"
]
}

Sample JSON B


{
"domain": {
"categories": [
{
"confidence": 0.92,
"name": "/Computers & Electronics/Consumer Electronics",
"IAB-632-596": "Consumer Electronics/Technology & Computing/Consumer Electronics"
},
{
"confidence": 0.89,
"name": "/Internet & Telecom/Mobile & Wireless/Mobile Phones"
}
],
"social_media": null,
"logo_url": "https://klazify.s3.amazonaws.com/2110787991611585019600ed5fb1d1300.04730104.png"
},
"success": true,
"objects": {
"company": {
"url": "https://www.apple.com/",
"name": "Apple",
"city": "Cupertino",
"stateCode": "CA",
"countryCode": "US",
"employeesRange": "100K+",
"revenue": 274515000000,
"raised": null,
"tags": [
"E-commerce",
"Consumer Electronics",
"Mobile",
"B2C"
],
"tech": [
"omniture_adobe_analytics",
"atlassian_confluence",
"successfactors",
"apache_apex",
"talend",
"oracle_peoplesoft",
"salesforce",
"stripe",
"dell_boomi_atomsphere",
"gigya",
"sage_50cloud",
"quickbooks",
"webmethods",
"apache_tomcat",
"alteryx",
"tibco_rendezvous",
"atlassian_jira",
"..."
]
}
},
"domain_registration_data": {
"domain_age_date": "1987-02-19",
"domain_age_days_ago": "13026",
"domain_expiration_date": "2030-02-20",
"domain_expiration_days_left": "123"
},
"similar_domains": [
"bestbuy.com",
"icloud.com",
"microsoft.com",
"macrumors.com",
"google.com",
"samsung.com",
"twitter.com",
"hp.com",
"bhphotovideo.com",
"dell.com"
]
}

Sample JSON C


{
"domain": {
"categories": [
{
"confidence": 0.92,
"name": "/Computers & Electronics/Consumer Electronics",
"IAB-632-596": "Consumer Electronics/Technology & Computing/Consumer Electronics"
},
{
"confidence": 0.89,
"name": "/Internet & Telecom/Mobile & Wireless/Mobile Phones"
}
],
"social_media": null,
"logo_url": "https://klazify.s3.amazonaws.com/2110787991611585019600ed5fb1d1300.04730104.png"
},
"success": true,
"objects": {
"company": {
"url": "https://www.apple.com/",
"name": "Apple",
"city": "Cupertino",
"stateCode": "CA",
"countryCode": "US",
"employeesRange": "100K+",
"revenue": 274515000000,
"raised": null,
"tags": [
"E-commerce",
"Consumer Electronics",
"Mobile",
"B2C"
],
"tech": [
"omniture_adobe_analytics",
"atlassian_confluence",
"successfactors",
"apache_apex",
"talend",
"oracle_peoplesoft",
"salesforce",
"stripe",
"dell_boomi_atomsphere",
"gigya",
"sage_50cloud",
"quickbooks",
"webmethods",
"apache_tomcat",
"alteryx",
"tibco_rendezvous",
"atlassian_jira",
"..."
]
}
},
"domain_registration_data": {
"domain_age_date": "1987-02-19",
"domain_age_days_ago": "13026",
"domain_expiration_date": "2030-02-20",
"domain_expiration_days_left": "123"
},
"similar_domains": [
"bestbuy.com",
"icloud.com",
"microsoft.com",
"macrumors.com",
"google.com",
"samsung.com",
"twitter.com",
"hp.com",
"bhphotovideo.com",
"dell.com"
]
}

Map categories to a Crime & Justice policy decision (Python)

The snippet below shows how to call the endpoint and map categories to a binary decision. Configure the set of category names and/or IAB mappings that your policy treats as Crime & Justice. To keep this guide aligned with the provided data, the code references your own configuration rather than enumerating category names here.

import os
import json
import time
import requests
from urllib.parse import urlparse

KLAZIFY_URL = "https://www.klazify.com/api/categorize"
API_KEY = os.getenv("KLAZIFY_API_KEY", "YOUR_API_KEY")

# Configure this in code or load from a rules service.
# For example, store canonical category names or IAB mappings you consider "Crime & Justice".
CRIME_JUSTICE_CATEGORY_NAMES = set([
# e.g., "/News & Politics/Crime", "/Law & Government/Justice System", ...
# Keep these in your own config; not listed here to avoid inventing category names.
])

CRIME_JUSTICE_IAB_CODES = set([
# e.g., "IABX-YYY" or descriptive IAB mapping strings used by your moderation policy.
# Keep this in your own config.
])

def normalize_domain(url: str) -> str:
parsed = urlparse(url.strip())
host = parsed.netloc or parsed.path # accept bare domains
return host.lower()

def is_crime_justice_category(categories):
for c in categories or []:
name = c.get("name", "")
if name in CRIME_JUSTICE_CATEGORY_NAMES:
return True
# Check any IAB-like mapping when present
for k, v in c.items():
if k.startswith("IAB") and v in CRIME_JUSTICE_IAB_CODES:
return True
return False

def classify_url(url: str):
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
}
payload = {"url": url}
r = requests.post(KLAZIFY_URL, headers=headers, data=json.dumps(payload), timeout=20)
r.raise_for_status()
data = r.json()
return data

def decision_for_url(url: str, min_confidence: float = 0.75):
data = classify_url(url)
cats = (data.get("domain") or {}).get("categories") or []

# Optional: apply a confidence threshold across the top category
top_conf = max((c.get("confidence", 0.0) for c in cats), default=0.0)

cj = is_crime_justice_category(cats)
is_review = cj and (top_conf < min_confidence)

decision = "BLOCK" if cj and top_conf >= min_confidence else ("REVIEW" if is_review else "ALLOW")

return {
"decision": decision,
"top_confidence": top_conf,
"categories": cats,
"logo_url": (data.get("domain") or {}).get("logo_url"),
"company": (data.get("objects") or {}).get("company"),
"domain_meta": data.get("domain_registration_data"),
"raw": data,
}

if __name__ == "__main__":
test_url = "https://www.justice.gov"
out = decision_for_url(test_url, min_confidence=0.75)
print(json.dumps(out, indent=2))

How this turns API data into an action:

is_crime_justice_category checks the category names and any IAB mapping fields returned under each category object. You supply the exact set you consider to be Crime & Justice.
decision_for_url returns one of ALLOW, REVIEW, or BLOCK based on your policy and the maximum confidence in the response.
You can persist the parsed signals (logo_url, company, domain_registration_data) for your internal audit interface without re-calling the API.

Which fields to read for production decisions

Field	Purpose	Typical Use in Crime & Justice Policy
domain.categories[].name	Human-readable taxonomy path	Primary allow/deny matching against your Crime & Justice list
domain.categories[].confidence	Probability-like score	Thresholding (e.g., above 0.75: BLOCK; between 0.50–0.75: REVIEW)
domain.categories[].IAB-...	IAB taxonomy mapping	Optional secondary rule when your workflow depends on IAB alignment
objects.company.*	Business context	Display on internal review panels; avoid using alone for category decisions
domain.logo_url	Brand recognition	UI element for reviewers to quickly recognize sites
domain_registration_data.*	Registrar timelines	Escalate newly registered domains for additional review
similar_domains[]	Related properties	Queue adjacent domains for preemptive checks or safety audits

Operational playbook: scale, cache, and privacy

Caching per domain

Cache the decision keyed by the normalized registrable domain (e.g., example.gov). Many sites have stable categorical signals across pages. Suggested TTLs vary by your risk profile; shorter TTLs for news-like properties, longer for static organizations.

Cache key: registrable_domain + policy_version + min_confidence.
Cache value: final decision + snapshot of categories + timestamps for audit.

Handling unknown or brand-new domains

If the categories array is empty or confidence is low, route to REVIEW rather than ALLOW or BLOCK. This prevents accidental overblocking or underblocking for new content.
Use domain_registration_data to detect young domains and treat them conservatively (e.g., reduced TTL and mandatory review).

Batching and rate limits

Batch via a job queue that deduplicates domains and fans out workers within your plan’s rate limits. Avoid synchronous N-by-1 calls in web request paths.
Use backoff and retry with idempotency keys in your job payloads so reprocessing is safe.
Log response times and worker QPS to right-size concurrency against your allocated throughput.

Mapping to your taxonomy

Create a controlled list of category names that your policy treats as Crime & Justice. Keep this list versioned and auditable.
Optionally add IAB mapping-based rules where an IAB-aligned tag is required to trigger BLOCK.
Store both the raw category string and your internal normalized label to support future reclassification without replaying all URLs.

Auditability

Persist: input URL, normalized domain, timestamp, categories, top confidence, decision, and rule version.
Provide reviewers: company.name, logo_url, and a link to the live page for context (if policy allows).
Allow overrides: a manual whitelist/blacklist that the decision engine consults before any API call.

Privacy and compliance considerations

When building Crime & Justice classification, protect user privacy and meet your regulatory requirements:

Minimize data in transit: send only the URL or domain needed; avoid attaching user identifiers to the API call.
Pseudonymize inputs: where possible, hash internal user IDs stored alongside classification events.
Retention limits: store only the fields necessary for audit and policy enforcement; apply TTL policies to purge historical logs.
Access controls: restrict who can query classification history; review logs for escalated domains only.
Regional routing: if your compliance model requires regional handling, run classification from compliant environments and segregate caches accordingly.

For enterprise controls and process alignment, review the MCP page and the API Documentation.

End-to-end example: URL intake to decision cache

1) Intake

Receive a candidate URL (e.g., a site where your ad might be placed or a domain entered during signup).
Normalize to the registrable domain for caching, but keep the full URL for page-level checks if your policy demands it.

2) Cache read

Check the domain + policy_version cache. If present and fresh, use the cached decision.
If stale or missing, proceed to call the categorize endpoint.

3) Categorize call

POST the URL. Capture latency and HTTP status for observability.
Parse domain.categories, read confidence and any IAB mapping, and compute a decision against your Crime & Justice list.

4) Decision, write-back, and action

Store: categories, confidence, decision, timestamp, and rule version in the cache and audit store.
Apply: block the ad placement, flag the signup, or pass with ALLOW depending on the result.
Notify: if REVIEW is returned, queue for human moderation with company and logo context.

Troubleshooting common patterns

Mixed-content domains: If a large publisher hosts a Crime & Justice section, consider page-level classification for specific placements, but cache domain-level results for other workflows.
Low confidence categories: Route to REVIEW and increase sampling frequency; you can also recheck in a few hours if the page is rapidly evolving (e.g., breaking news).
Unexpected category mappings: Log both the name and IAB mapping fields you used to trigger policy. Adjust your mapping rules with a version bump for full traceability.

SRE checklist for productionizing this flow

Metrics: p50/p95 response times, timeout ratio, error ratio by HTTP code, and cache hit rate.
Alerts: high timeout ratio, error spikes, or sudden shifts in category distributions (possible upstream changes or traffic anomalies).
Backoff: exponential retry with jitter for transient errors; circuit-breaker around the classify step to protect your request path.
Capacity: queue depth and worker concurrency tuned to your plan and target latency SLO.
Security: rotate API keys and scope access to secrets.

Recap: what matters for Crime & Justice classification

Use domain.categories for deterministic, explainable decisions.
Leverage confidence to separate BLOCK from REVIEW.
Cache aggressively per domain with a reasonable TTL and policy versioning.
Respect privacy: limit data sharing, retain only what you need, and secure audit access.

When you’re ready to implement, create a free account at Register and explore the full Documentation. You can also learn more about Klazify at https://www.klazify.com and share that link with your team’s reviewers to align on taxonomy and process.

FAQ

How do I decide between domain-level and page-level classification?
If your policy is sensitive to sections (e.g., only some parts of a site are Crime & Justice), use page-level classification. For signup enrichment or broad brand safety, domain-level is usually sufficient and easier to cache.
What if a domain returns no categories or very low confidence?
Route to REVIEW and add a shorter TTL so the domain is rechecked soon. You can also increase observability for such domains and prioritize them for manual decisions.
How should I build my list of Crime & Justice categories?
Start with a controlled list that maps the category name strings and optional IAB mappings you consider in-scope. Version the list and store it in a central configuration service so changes are auditable and can be rolled out safely.
Can I enrich CRM records using the same response?
Yes. Use objects.company fields (name, location, employeesRange, revenue, tags) to supplement CRM profiles, and store logo_url for UI components. Keep category-based policy decisions separate from enrichment to avoid conflating signals.
How do I avoid repeatedly sending user data to an external service?
Cache decisions by domain, retain only the minimal fields required, and avoid including user identifiers in API calls. Apply strict TTLs and access controls on your classification logs.

Ready to implement a reliable Crime & Justice classification workflow? Create your free account now and start integrating the categorize endpoint: Register. For complete reference material and endpoint details, visit the Documentation and learn more at https://www.klazify.com.