Auditing URL Classification API Decisions in Crime & Justice

You need to audit how a URL classification API flags Crime & Justice content across your inventory so you can block certain categories for brand safety, allowlist others for educational or news use, and produce a traceable decision log. By the end of this guide, you’ll be able to call Klazify’s categorize endpoint, interpret the returned categories (including IAB mappings), map them to your policy, and run a repeatable audit workflow you can plug into your pipeline.

Why Klazify fits Crime & Justice auditing

Auditing Crime & Justice content is nuanced: you may need to distinguish law enforcement resources and court information from breaking crime news or advocacy pages, and to support multiple languages. Klazify’s features line up well with this task:

• Accurate website categorization using AI: models analyze page content, not just metadata, so it can catch Crime & Justice context embedded in articles, court dockets, or policy pages rather than relying on domain labels alone.
• Global coverage: content is analyzed across languages, which matters when you audit international legal institutions, regional newsrooms, or cross-border safety initiatives.
• Real-time classification: you can recheck pages when content changes (e.g., a homepage switches to a breaking crime story) rather than relying on static databases.
• Industry-level categories via IAB taxonomy: decisions are traceable to a standard hierarchy you can align with your brand safety policy or compliance rules.
• Simple API integration: one REST call returns categories, IAB mapping codes, company data, logos, and related signals you can store as audit evidence.
• Compliance and filtering: the output supports building allow/block/needs-review decisions for Crime & Justice auditing, with confidence scores you can threshold.

If you’re starting fresh, you can create a free account and get an API key here: Register. For complete parameter reference, see the Documentation. You can also explore additional capabilities via MCP.

The audit scenario and the decisions you need

Three common goals drive Crime & Justice classification audits:

• Brand safety blocking: exclude categories tied to crime topics from ad placements while allowing official resources or general news.
• Content filtering: restrict access to certain Crime & Justice pages on corporate or educational networks while permitting court or government information.
• Data enrichment: tag signups or domains in your CRM if their websites lean into legal services, justice advocacy, or law enforcement content.

In all cases, you need consistent rules you can prove later. That means:

• Always log the categories returned, their confidence, and the IAB mapping.
• Apply a repeatable mapping to produce a binary (allow/block) or ternary (allow/block/needs-review) decision.
• Cache results per domain to control cost and latency, then recheck on a schedule or when content is likely to change.

Classify a URL with a single POST

Use the categorize endpoint to classify a specific URL. The request includes your bearer token and the URL you want to audit. For Crime & Justice auditing, you might classify a law enforcement or court page to see how it’s categorized.

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 the exact page you want to classify for page-level precision (e.g., a specific article URL), or a homepage if you want broad domain-level behavior.
• Batch processing can be done by iterating over your URL list in your job runner. Respect concurrency and retry policies in your environment; see the Documentation for details.

Example response and what to read for auditing

The following is an official sample response. Use it to learn the field structure you will parse in your audit pipeline.

{
"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 apply these fields for Crime & Justice auditing:

• domain.categories: the core of your decision. Each item has a category path in name and an optional IAB taxonomy mapping. Use confidence to gate your decision (e.g., only enforce rules above a threshold, otherwise flag for review).
• IAB-632-596: an example of an IAB mapping key-value. When present, use it to align decisions to your IAB-based brand safety or compliance rules.
• objects.company: enrichment data. For audits, you can store company.name, company.url, and tags as evidence, but base allow/block decisions on domain.categories to avoid bias from brand identity alone.
• logo_url: handy for UI or analyst review workflows.
• domain_registration_data: use for secondary signals in your review queue (e.g., very new domains could require a manual check).
• similar_domains: optional context to expand your audit to neighboring sites, then classify them with the same endpoint.

Map categories to allow/block decisions in Python

Below is a Python example that classifies a URL, reads domain.categories, and maps them to a three-way decision (allow, block, needs_review). The block/allow rules are maintained as your internal policy terms and IAB pattern matches. Adjust the keyword and IAB lists based on your compliance program.

import json
import os
import sys
import time
import urllib.request

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

# Policy configuration:
CONFIDENCE_THRESHOLD = 0.80 # enforce when at or above this
CRIME_JUSTICE_KEYWORDS = {
# Populate with the category name substrings you treat as Crime & Justice signals.
# Examples (adjust to your policy): "Crime", "Justice", "Law", "Court", "Police", "Corrections"
"Crime", "Justice", "Law", "Court", "Police", "Corrections"
}
IAB_BLOCKLIST = {
# Populate with IAB taxonomy strings you treat as Crime & Justice signals.
# Examples (adjust to your policy): "Crime", "Law", "Public Safety"
"Crime", "Law", "Public Safety"
}

def categorize(url):
payload = json.dumps({"url": url}).encode("utf-8")
req = urllib.request.Request(
ENDPOINT,
data=payload,
headers={
"Authorization": f"Bearer {KLAZIFY_API_KEY}",
"Content-Type": "application/json",
"Accept": "application/json",
},
method="POST",
)
with urllib.request.urlopen(req, timeout=30) as resp:
data = json.loads(resp.read().decode("utf-8"))
return data

def decide(audit_json):
cats = (audit_json.get("domain") or {}).get("categories") or []
hits = []
for c in cats:
name = c.get("name") or ""
conf = float(c.get("confidence") or 0.0)
# Collect any IAB mapping key/values present in the category object
iab_values = [v for k, v in c.items() if k.startswith("IAB-")]
# Evaluate category name against policy keywords
name_hit = any(term.lower() in name.lower() for term in CRIME_JUSTICE_KEYWORDS)
# Evaluate IAB mapping strings against policy
iab_hit = any(any(term.lower() in (iab or "").lower() for term in IAB_BLOCKLIST) for iab in iab_values)

if (name_hit or iab_hit):
hits.append({"name": name, "confidence": conf, "iab": iab_values})

# Decision logic:
# - If any hit has confidence >= threshold, block
# - If hits exist but below threshold, needs_review
# - Otherwise, allow
if any(h["confidence"] >= CONFIDENCE_THRESHOLD for h in hits):
decision = "block"
elif hits:
decision = "needs_review"
else:
decision = "allow"

evidence = {
"categories": cats,
"hits": hits,
"threshold": CONFIDENCE_THRESHOLD,
"logo_url": (audit_json.get("domain") or {}).get("logo_url"),
"company": (audit_json.get("objects") or {}).get("company"),
"domain_registration_data": audit_json.get("domain_registration_data"),
"similar_domains": audit_json.get("similar_domains"),
}
return decision, evidence

if __name__ == "__main__":
test_url = sys.argv[1] if len(sys.argv) > 1 else "https://www.justice.gov/"
result = categorize(test_url)
decision, evidence = decide(result)
print(json.dumps({
"url": test_url,
"decision": decision,
"evidence": evidence,
"timestamp": int(time.time())
}, indent=2))

How to use this in your pipeline:

• Set KLAZIFY_API_KEY in your environment and call the script with the target URL.
• Tune CONFIDENCE_THRESHOLD to your risk tolerance. Higher thresholds reduce false blocks but increase manual reviews.
• Maintain CRIME_JUSTICE_KEYWORDS and IAB_BLOCKLIST within your policy repo so policy updates don’t require code changes.
• Persist the evidence object in your audit log. This makes every decision explainable and reproducible.

What to store and how to act on it

• Decision fields: decision (allow/block/needs_review), threshold used, categories returned, and any IAB mapping hits.
• Provenance fields: input URL, timestamp, your application/user/process making the call.
• Evidence snapshots: logo_url, objects.company subset, domain_registration_data, and similar_domains to support analyst review.
• Re-run logic: if a decision was needs_review due to low confidence, schedule a recheck after content may have changed (e.g., 24–48 hours).

Operational details that save time

Caching per domain and page

Cache classifications keyed by normalized URL (page-level) and also by registrable domain (domain-level). Many homepages and stable resource pages change slowly; recheck on a cadence you define. In ad tech or brand safety flows, a short TTL (e.g., hours) for article URLs and a longer TTL (e.g., days) for domains balances freshness and cost. Always invalidate the cache when significant site updates are detected on your side.

Handling unknown or new domains

• If categories are empty or confidence is low, route to needs_review and store the full JSON for an analyst. Keep a backlog job that retries classification later.
• Use domain_registration_data as a soft signal: a very recent domain could be automatically flagged for manual verification regardless of initial categories.

Batching and throughput

• Batch by iterating through URLs in your job runner and posting to the same endpoint. Keep concurrency at a level that’s healthy for your environment and follow guidance in the Documentation.
• Use exponential backoff on transient network errors and persist partial results after each batch to avoid losing audit evidence.
• De-duplicate within a batch so the same URL isn’t requested multiple times; rely on your cache to short-circuit repeats.

Mapping to your internal taxonomy

• Store the raw category name and any IAB mapping from the response.
• Maintain a simple mapping table that translates each known category path or IAB string to your internal labels (e.g., “CRIME_JUSTICE_NEWS”, “LAW_ENFORCEMENT_RESOURCES”). Keep this table versioned.
• During audits, log both the external (IAB) and internal labels to preserve traceability.

Latency and timeouts

• Set client timeouts appropriate for your backend (the Python example uses 30s). In synchronous user flows, wrap the call in a circuit breaker and degrade gracefully to a temporary allow or review state based on policy.
• In asynchronous pipelines, capture the decision once available and tag any downstream records (ad requests, content items) with the result.

Auditing workflow you can ship

1. Input normalization: canonicalize URLs (scheme, host, path) and deduplicate.
2. Cache lookup: return cached decision if fresh; otherwise call the categorize endpoint.
3. Decision mapping: evaluate domain.categories and any IAB mapping against your policy and confidence threshold.
4. Evidence log: store JSON response, the decision, threshold, and policy version used.
5. Actions:
   • Brand safety: block ad serving for “block”, allow for “allow”, and queue human review for “needs_review”.
   • Content filtering: block or permit page loads per decision; optionally show reason codes for internal users.
   • Enrichment: tag CRM or analytics records with internal labels derived from categories/IAB mapping.
6. Recheck: for “needs_review”, schedule a reclassification or let an analyst override and write back the outcome.

What each field does in your decision

Practical tips for stable operations

• Normalize domains: lowercasing, stripping tracking params when possible, and using a consistent canonical representation improves cache hits.
• Policy versioning: include a policy_version field in your audit log; when your mapping changes, you can rejudge past URLs without re-calling the API.
• Explainability: store the raw JSON response so any future audit can reconstruct the decision context.
• Partial data: code defensively for nulls (e.g., social_media may be null) and missing IAB mapping keys on some categories.

Where to go next

Set up your key and run your first classification against a handful of URLs you suspect are Crime & Justice related. Iterate on your policy keywords and IAB mapping until your allow/block outcomes match your organization’s requirements. Keep a tight cache, log everything, and schedule rechecks to adapt to changing content. You can explore capabilities and update your integration anytime at https://www.klazify.com, and get full reference details in the Documentation.

FAQ

How should I pick a confidence threshold for blocking?
Start conservatively (e.g., require higher confidence to auto-block) and route lower-confidence hits to manual review. Track false positives/negatives over time and adjust your threshold where it minimizes review load without letting unwanted content through.

Should I classify the domain or the exact page?
For brand safety on article-based sites, classify the specific URL. For general filtering or enrichment where the site’s overall topic matters, domain-level classification is usually sufficient. Cache both when you do both kinds of tasks.

What if there’s no IAB mapping key in a category object?
Use the category name and confidence to decide. Your internal mapping can key off either the name string or any IAB mapping when present; code defensively for missing IAB fields.

How do I handle very new or unknown domains?
If categories are empty or confidence is below threshold, mark needs_review and optionally raise priority if the domain is very new per domain_registration_data. Schedule an automatic recheck to avoid stale outcomes.

Can I enrich CRM records with this data?
Yes. Store domain.categories, internal labels mapped from them, and selected objects.company fields (name, url, tags) to make your CRM segments and lead scoring more context-aware.

Ready to audit your Crime & Justice classifications with a single API call? Create your free account and get an API key now: Register. For a deeper dive into endpoints and response fields, visit klazify.com and the full Documentation.