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PATCH
/
api
/
violations
/
{id}
curl -X PATCH "https://api.example.com/api/violations/viol_789xyz" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "status": "false_positive",
    "review_note": "This transaction was pre-authorized by compliance team"
  }'

{
  "success": true,
  "violation": {
    "id": "viol_789xyz",
    "status": "false_positive",
    "reviewed_at": "2024-02-28T14:30:00Z"
  },
  "updated_score": 87.5
}
Review a compliance violation by marking it as approved or a false positive. This triggers:
  1. Compliance score recalculation for the scan
  2. Bayesian feedback loop - updates the parent rule’s accuracy statistics
  3. Score history tracking for compliance trend analysis

​
Path Parameters

​
id
string
required
Unique violation identifier

​
Request Body

​
status
string
required
Review decision. Valid values:
  • approved - Confirms the violation is legitimate
  • false_positive - Marks the violation as incorrectly flagged
​
review_note
string
Optional notes explaining the review decision

​
Response

​
success
boolean
Indicates whether the review was successful
​
violation
object
Updated violation data
​
id
string
Violation identifier
​
status
string
New status: approved or false_positive
​
reviewed_at
string
ISO 8601 timestamp when the review was completed
​
updated_score
number
New compliance score for the scan (0-100)
curl -X PATCH "https://api.example.com/api/violations/viol_789xyz" \
  -H "Authorization: Bearer YOUR_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "status": "false_positive",
    "review_note": "This transaction was pre-authorized by compliance team"
  }'

{
  "success": true,
  "violation": {
    "id": "viol_789xyz",
    "status": "false_positive",
    "reviewed_at": "2024-02-28T14:30:00Z"
  },
  "updated_score": 87.5
}

​
Bayesian Feedback Loop

When you review a violation, the system automatically updates the parent rule’s statistics:
  • Approved violations → Increments approved_count for the rule
  • False positives → Increments false_positive_count for the rule
These counts are used to calculate rule accuracy scores: 
accuracy = approved_count / (approved_count + false_positive_count)
This Bayesian feedback mechanism allows the system to:
  1. Learn which rules are most accurate over time
  2. Adjust confidence scores for future violations
  3. Prioritize high-accuracy rules in scan results
  4. Flag rules that generate excessive false positives for tuning

​
Score History Tracking

Each review creates an entry in the scan’s score_history array: 
{
  "score": 87.5,
  "timestamp": "2024-02-28T14:30:00Z",
  "action": "false_positive",
  "violation_id": "viol_789xyz"
}
This enables compliance trend visualization and audit trails.

​
Compliance Score Recalculation

The compliance score is automatically recalculated based on:
  • Total number of records scanned
  • Severity distribution of active violations (excluding false positives)
  • Weighted penalties: CRITICAL (10 points), HIGH (5 points), MEDIUM (2 points)
Score formula: 
score = max(0, 100 - totalPenalty)

​
Error Responses

​
error
string
Error code: VALIDATION_ERROR, NOT_FOUND, UNAUTHORIZED, or INTERNAL_ERROR
​
message
string
Human-readable error message
​
details
array
Validation error details (only for VALIDATION_ERROR)

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