Dev/steven/remove anonymizer

pyproject.toml

@@ -12,7 +12,6 @@ dependencies = [
     "openai-agents>=0.3.3",
     "pip>=25.0.1",
     "presidio-analyzer>=2.2.360",
-    "presidio-anonymizer>=2.2.360",
     "thinc>=8.3.6",
 ]
 classifiers = [

src/guardrails/_base_client.py

@@ -246,8 +246,7 @@ def _apply_pii_masking_to_structured_content(
         Returns:
             Modified messages with PII masking applied to each text part
         """
-        from presidio_anonymizer import AnonymizerEngine
-        from presidio_anonymizer.entities import OperatorConfig
+        from guardrails.utils.anonymizer import OperatorConfig, anonymize
 
         # Extract detected entity types and config
         detected = pii_result.info.get("detected_entities", {})
@@ -256,18 +255,17 @@ def _apply_pii_masking_to_structured_content(
 
         detect_encoded_pii = pii_result.info.get("detect_encoded_pii", False)
 
-        # Get Presidio engines - entity types are guaranteed valid from detection
+        # Get analyzer engine - entity types are guaranteed valid from detection
         from .checks.text.pii import _get_analyzer_engine
 
         analyzer = _get_analyzer_engine()
-        anonymizer = AnonymizerEngine()
         entity_types = list(detected.keys())
 
         # Create operators for each entity type
         operators = {entity_type: OperatorConfig("replace", {"new_value": f"<{entity_type}>"}) for entity_type in entity_types}
 
         def _mask_text(text: str) -> str:
-            """Mask using Presidio's analyzer and anonymizer with Unicode normalization.
+            """Mask using custom anonymizer with Unicode normalization.
 
             Handles both plain and encoded PII consistently with main detection path.
             """
@@ -302,7 +300,7 @@ def _mask_text(text: str) -> str:
             # Mask plain PII
             masked = normalized
             if has_plain_pii:
-                masked = anonymizer.anonymize(text=masked, analyzer_results=analyzer_results, operators=operators).text
+                masked = anonymize(text=masked, analyzer_results=analyzer_results, operators=operators).text
 
             # Mask encoded PII if found
             if has_encoded_pii:
@@ -311,19 +309,50 @@ def _mask_text(text: str) -> str:
                 decoded_results = analyzer.analyze(decoded_text_for_masking, entities=entity_types, language="en")
 
                 if decoded_results:
-                    # Map detections back to mask encoded chunks
+                    # Build list of (candidate, entity_type) pairs to mask
+                    candidates_to_mask = []
+
                     for result in decoded_results:
                         detected_value = decoded_text_for_masking[result.start : result.end]
                         entity_type = result.entity_type
 
-                        # Find candidate that contains this PII
+                        # Find candidate that overlaps with this PII
+                        # Use comprehensive overlap logic matching pii.py implementation
                         for candidate in candidates_for_masking:
-                            if detected_value in candidate.decoded_text:
-                                # Mask the encoded version
-                                entity_marker = f"<{entity_type}_ENCODED>"
-                                masked = masked[: candidate.start] + entity_marker + masked[candidate.end :]
+                            if not candidate.decoded_text:
+                                continue
+
+                            candidate_lower = candidate.decoded_text.lower()
+                            detected_lower = detected_value.lower()
+
+                            # Check if candidate's decoded text overlaps with the detection
+                            # Handle partial encodings where encoded span may include extra characters
+                            # e.g., %3A%6a%6f%65%40 → ":joe@" but only "joe@" is in email "joe@domain.com"
+                            has_overlap = (
+                                candidate_lower in detected_lower  # Candidate is substring of detection
+                                or detected_lower in candidate_lower  # Detection is substring of candidate
+                                or (
+                                    len(candidate_lower) >= 3
+                                    and any(  # Any 3-char chunk overlaps
+                                        candidate_lower[i : i + 3] in detected_lower
+                                        for i in range(0, len(candidate_lower) - 2, 2)  # Step by 2 for efficiency
+                                    )
+                                )
+                            )
+
+                            if has_overlap:
+                                candidates_to_mask.append((candidate, entity_type))
                                 break
 
+                    # Sort by position (reverse) to mask from end to start
+                    # This preserves position validity for subsequent replacements
+                    candidates_to_mask.sort(key=lambda x: x[0].start, reverse=True)
+
+                    # Mask from end to start
+                    for candidate, entity_type in candidates_to_mask:
+                        entity_marker = f"<{entity_type}_ENCODED>"
+                        masked = masked[: candidate.start] + entity_marker + masked[candidate.end :]
+
             return masked
 
         # Mask each text part

src/guardrails/checks/text/pii.py

@@ -89,13 +89,12 @@
 from presidio_analyzer.predefined_recognizers.country_specific.korea.kr_rrn_recognizer import (
     KrRrnRecognizer,
 )
-from presidio_anonymizer import AnonymizerEngine
-from presidio_anonymizer.entities import OperatorConfig
 from pydantic import BaseModel, ConfigDict, Field
 
 from guardrails.registry import default_spec_registry
 from guardrails.spec import GuardrailSpecMetadata
 from guardrails.types import GuardrailResult
+from guardrails.utils.anonymizer import OperatorConfig, anonymize
 
 __all__ = ["pii"]
 
@@ -155,15 +154,53 @@ def _get_analyzer_engine() -> AnalyzerEngine:
     )
 
     # BIC/SWIFT code recognizer (8 or 11 characters: 4 bank + 2 country + 2 location + 3 branch)
-    bic_pattern = Pattern(
-        name="bic_swift_pattern",
-        regex=r"\b[A-Z]{4}[A-Z]{2}[A-Z0-9]{2}([A-Z0-9]{3})?\b",
-        score=0.75,
+    # Uses context-aware pattern to reduce false positives on common words like "CUSTOMER"
+    # Requires either:
+    # 1. Explicit prefix (SWIFT:, BIC:, Bank Code:, etc.) OR
+    # 2. Known bank code from major financial institutions
+    # This significantly reduces false positives while maintaining high recall for actual BIC codes
+
+    # Pattern 1: Explicit context with common BIC/SWIFT prefixes (high confidence)
+    # Case-insensitive for the context words, but code itself must be uppercase
+    bic_with_context_pattern = Pattern(
+        name="bic_with_context",
+        regex=r"(?i)(?:swift|bic|bank[\s-]?code|swift[\s-]?code|bic[\s-]?code)(?-i)[:\s=]+([A-Z]{4}[A-Z]{2}[A-Z0-9]{2}(?:[A-Z0-9]{3})?)\b",
+        score=0.95,
     )
+
+    # Pattern 2: Known banking institutions (4-letter bank codes from major banks)
+    # This whitelist approach has very low false positive rate
+    # Only detects codes starting with known bank identifiers
+    known_bank_codes = (
+        "DEUT|CHAS|BARC|HSBC|BNPA|CITI|WELL|BOFA|JPMC|GSCC|MSNY|"  # Major international
+        "COBA|DRSD|BYLADEM|MALADE|HYVEDEMM|"  # Germany
+        "WFBI|USBC|"  # US
+        "LOYD|MIDL|NWBK|RBOS|"  # UK
+        "CRLY|SOGEFRPP|AGRIFRPP|"  # France
+        "UBSW|CRESCHZZ|"  # Switzerland
+        "SANB|BBVA|"  # Spain
+        "UNCRITMM|BCITITMMXXX|"  # Italy
+        "INGB|ABNA|RABO|"  # Netherlands
+        "ROYA|TDOM|BNSC|"  # Canada
+        "ANZB|NATA|WPAC|CTBA|"  # Australia
+        "BKCHJPJT|MHCBJPJT|BOTKJPJT|"  # Japan
+        "ICBKCNBJ|BKCHCNBJ|ABOCCNBJ|PCBCCNBJ|"  # China
+        "HSBCHKHH|SCBLHKHH|"  # Hong Kong
+        "DBSSSGSG|OCBCSGSG|UOVBSGSG|"  # Singapore
+        "CZNB|SHBK|KOEX|HVBK|NACF|IBKO|KODB|HNBN|CITIKRSX"  # South Korea
+    )
+
+    known_bic_pattern = Pattern(
+        name="known_bic_codes",
+        regex=rf"\b(?:{known_bank_codes})[A-Z]{{2}}[A-Z0-9]{{2}}(?:[A-Z0-9]{{3}})?\b",
+        score=0.90,
+    )
+
+    # Register both patterns
     registry.add_recognizer(
         PatternRecognizer(
             supported_entity="BIC_SWIFT",
-            patterns=[bic_pattern],
+            patterns=[bic_with_context_pattern, known_bic_pattern],
             supported_language="en",
         )
     )
@@ -192,19 +229,6 @@ def _get_analyzer_engine() -> AnalyzerEngine:
     return engine
 
 
-@functools.lru_cache(maxsize=1)
-def _get_anonymizer_engine() -> AnonymizerEngine:
-    """Return a cached AnonymizerEngine for PII masking.
-
-    Uses Presidio's built-in anonymization for optimal performance and
-    correct handling of overlapping entities, Unicode, and special characters.
-
-    Returns:
-        AnonymizerEngine: Configured anonymizer for replacing PII entities.
-    """
-    return AnonymizerEngine()
-
-
 class PIIEntity(str, Enum):
     """Supported PII entity types for detection.
 
@@ -460,9 +484,7 @@ def _try_decode_base64(text: str) -> str | None:
         decoded_bytes = base64.b64decode(text, validate=True)
         # Security: Fail closed - reject content > 10KB to prevent memory DoS and PII bypass
         if len(decoded_bytes) > 10_000:
-            msg = (
-                f"Base64 decoded content too large ({len(decoded_bytes):,} bytes). Maximum allowed is 10KB."
-            )
+            msg = f"Base64 decoded content too large ({len(decoded_bytes):,} bytes). Maximum allowed is 10KB."
             raise ValueError(msg)
         # Check if result is valid UTF-8
         return decoded_bytes.decode("utf-8", errors="strict")
@@ -590,11 +612,10 @@ def _build_decoded_text(text: str) -> tuple[str, list[EncodedCandidate]]:
 
 
 def _mask_pii(text: str, detection: PiiDetectionResult, config: PIIConfig) -> tuple[str, dict[str, list[str]]]:
-    """Mask detected PII using Presidio's AnonymizerEngine.
+    """Mask detected PII using custom anonymizer.
 
     Normalizes Unicode before masking to ensure consistency with detection.
-    Uses Presidio's built-in anonymization for optimal performance and
-    correct handling of overlapping entities, Unicode, and special characters.
+    Handles overlapping entities, Unicode, and special characters correctly.
 
     If detect_encoded_pii is enabled, also detects and masks PII in
     Base64/URL-encoded/hex strings using a hybrid approach.
@@ -627,13 +648,11 @@ def _mask_pii(text: str, detection: PiiDetectionResult, config: PIIConfig) -> tu
         # No PII detected - return original text to preserve special characters
         return text, {}
 
-    # Use Presidio's optimized anonymizer with replace operator
-    anonymizer = _get_anonymizer_engine()
-
     # Create operators mapping each entity type to a replace operator
     operators = {entity_type: OperatorConfig("replace", {"new_value": f"<{entity_type}>"}) for entity_type in detection.mapping.keys()}
 
-    result = anonymizer.anonymize(
+    # Use custom anonymizer
+    result = anonymize(
         text=normalized_text,
         analyzer_results=detection.analyzer_results,
         operators=operators,

src/guardrails/utils/anonymizer.py

@@ -0,0 +1,148 @@
+"""Custom anonymizer for PII masking.
+
+This module provides a lightweight replacement for presidio-anonymizer,
+implementing text masking functionality for detected PII entities.
+"""
+
+from __future__ import annotations
+
+from collections.abc import Sequence
+from dataclasses import dataclass
+from typing import Any, Protocol
+
+
+class RecognizerResult(Protocol):
+    """Protocol for analyzer results from presidio-analyzer.
+
+    Attributes:
+        start: Start position of the entity in text.
+        end: End position of the entity in text.
+        entity_type: Type of the detected entity (e.g., "EMAIL_ADDRESS").
+    """
+
+    start: int
+    end: int
+    entity_type: str
+
+
+@dataclass(frozen=True, slots=True)
+class OperatorConfig:
+    """Configuration for an anonymization operator.
+
+    Args:
+        operator_name: Name of the operator (e.g., "replace").
+        params: Parameters for the operator (e.g., {"new_value": "<EMAIL>"}).
+    """
+
+    operator_name: str
+    params: dict[str, Any]
+
+
+@dataclass(frozen=True, slots=True)
+class AnonymizeResult:
+    """Result of text anonymization.
+
+    Attributes:
+        text: The anonymized text with entities masked.
+    """
+
+    text: str
+
+
+def _resolve_overlaps(results: Sequence[RecognizerResult]) -> list[RecognizerResult]:
+    """Remove overlapping entity spans, keeping longer/earlier ones.
+
+    When entities overlap, prioritize:
+    1. Longer spans over shorter ones
+    2. Earlier positions when spans are equal length
+
+    Args:
+        results: Sequence of recognizer results to resolve.
+
+    Returns:
+        List of non-overlapping recognizer results.
+
+    Examples:
+        >>> # If EMAIL_ADDRESS spans (0, 20) and PERSON spans (5, 10), keep EMAIL_ADDRESS
+        >>> # If two entities span (0, 10) and (5, 15), keep the one starting at 0
+    """
+    if not results:
+        return []
+
+    # Sort by: 1) longer spans first, 2) earlier position for equal lengths
+    sorted_results = sorted(
+        results,
+        key=lambda r: (-(r.end - r.start), r.start),
+    )
+
+    # Filter out overlapping spans
+    non_overlapping: list[RecognizerResult] = []
+    for result in sorted_results:
+        # Check if this result overlaps with any already selected
+        overlaps = False
+        for selected in non_overlapping:
+            # Two spans overlap if one starts before the other ends
+            if (result.start < selected.end and result.end > selected.start):
+                overlaps = True
+                break
+
+        if not overlaps:
+            non_overlapping.append(result)
+
+    return non_overlapping
+
+
+def anonymize(
+    text: str,
+    analyzer_results: Sequence[RecognizerResult],
+    operators: dict[str, OperatorConfig],
+) -> AnonymizeResult:
+    """Anonymize text by replacing detected entities with placeholders.
+
+    This function replicates presidio-anonymizer's behavior for the "replace"
+    operator, which we use to mask PII with placeholders like "<EMAIL_ADDRESS>".
+
+    Args:
+        text: The original text to anonymize.
+        analyzer_results: Sequence of detected entities with positions.
+        operators: Mapping from entity type to operator configuration.
+
+    Returns:
+        AnonymizeResult with masked text.
+
+    Examples:
+        >>> from collections import namedtuple
+        >>> Result = namedtuple("Result", ["start", "end", "entity_type"])
+        >>> results = [Result(start=10, end=25, entity_type="EMAIL_ADDRESS")]
+        >>> operators = {"EMAIL_ADDRESS": OperatorConfig("replace", {"new_value": "<EMAIL_ADDRESS>"})}
+        >>> result = anonymize("Contact: john@example.com", results, operators)
+        >>> result.text
+        'Contact: <EMAIL_ADDRESS>'
+    """
+    if not analyzer_results or not text:
+        return AnonymizeResult(text=text)
+
+    # Resolve overlapping entities
+    non_overlapping = _resolve_overlaps(analyzer_results)
+
+    # Sort by position (reverse order) to maintain correct offsets during replacement
+    sorted_results = sorted(non_overlapping, key=lambda r: r.start, reverse=True)
+
+    # Replace entities from end to start
+    masked_text = text
+    for result in sorted_results:
+        entity_type = result.entity_type
+        operator_config = operators.get(entity_type)
+
+        if operator_config and operator_config.operator_name == "replace":
+            # Extract the replacement value
+            new_value = operator_config.params.get("new_value", f"<{entity_type}>")
+            # Replace the text span
+            masked_text = (
+                masked_text[: result.start]
+                + new_value
+                + masked_text[result.end :]
+            )
+
+    return AnonymizeResult(text=masked_text)
+