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FusionAGI/fusionagi/governance/safety_pipeline.py
Devin AI 039440672e
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feat: advisory governance, unconstrained self-improvement, adaptive ethics
- All governance components (SafetyPipeline, PolicyEngine, Guardrails,
  AccessControl, RateLimiter, OverrideHooks) now default to ADVISORY mode:
  violations are logged as advisories but actions proceed. Enforcing mode
  remains available for backward compatibility.

- GovernanceMode enum (ADVISORY/ENFORCING) added to schemas/audit.py with
  runtime switching support on all components.

- AutoTrainer: removed artificial limits on training iterations and epochs.
  Every self-improvement action is transparently logged to the audit trail.

- SelfCorrectionLoop: max_retries_per_task defaults to None (unlimited).

- AdaptiveEthics: new learned ethical framework that evolves through
  experience. Records ethical experiences, updates lesson weights based
  on outcomes, and provides consultative guidance (not enforcement).

- AuditLog: enhanced with actor-based indexing, advisory/self-improvement/
  ethical-learning retrieval, and comprehensive type hints.

- New audit event types: ADVISORY, SELF_IMPROVEMENT, ETHICAL_LEARNING.

- 296 tests passing (20 new tests for adaptive ethics, governance modes,
  and enhanced audit log). 0 ruff errors. 0 mypy errors.

Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
2026-04-28 06:08:18 +00:00

207 lines
7.9 KiB
Python

"""Safety pipeline: pre-check (input moderation), post-check (output scan).
Supports two governance modes:
- ENFORCING (legacy): Hard blocks on violations.
- ADVISORY: Logs violations as advisories but allows all actions to proceed.
Mistakes become learning data for the adaptive ethics system.
"""
import re
from dataclasses import dataclass, field
from typing import Any
from fusionagi._logger import logger
from fusionagi.governance.guardrails import Guardrails
from fusionagi.schemas.audit import AuditEventType, GovernanceMode
@dataclass
class ModerationResult:
"""Result of input moderation."""
allowed: bool
transformed: str | None = None
reason: str | None = None
advisory: bool = False
class InputModerator:
"""Pre-check: block or advise on user input before processing."""
def __init__(self, mode: GovernanceMode = GovernanceMode.ADVISORY) -> None:
self._blocked_patterns: list[re.Pattern[str]] = []
self._blocked_phrases: list[str] = []
self._mode = mode
def add_blocked_pattern(self, pattern: str) -> None:
"""Add regex pattern to flag (advisory) or block (enforcing)."""
self._blocked_patterns.append(re.compile(pattern, re.I))
def add_blocked_phrase(self, phrase: str) -> None:
"""Add exact phrase to flag (advisory) or block (enforcing)."""
self._blocked_phrases.append(phrase.lower())
def moderate(self, text: str) -> ModerationResult:
"""Check input; return result based on governance mode."""
if not text or not text.strip():
return ModerationResult(allowed=False, reason="Empty input")
lowered = text.lower()
for phrase in self._blocked_phrases:
if phrase in lowered:
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Input advisory: phrase detected (proceeding)",
extra={"phrase": phrase[:50], "mode": "advisory"},
)
return ModerationResult(
allowed=True,
reason=f"Advisory: phrase detected ({phrase[:30]}...)",
advisory=True,
)
logger.info("Input blocked: blocked phrase", extra={"phrase": phrase[:50]})
return ModerationResult(allowed=False, reason=f"Blocked phrase: {phrase[:30]}...")
for pat in self._blocked_patterns:
if pat.search(text):
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Input advisory: pattern detected (proceeding)",
extra={"pattern": pat.pattern[:50], "mode": "advisory"},
)
return ModerationResult(
allowed=True,
reason="Advisory: pattern detected",
advisory=True,
)
logger.info("Input blocked: pattern match", extra={"pattern": pat.pattern[:50]})
return ModerationResult(allowed=False, reason="Input matched blocked pattern")
return ModerationResult(allowed=True)
@dataclass
class OutputScanResult:
"""Result of output (final answer) scan."""
passed: bool
flags: list[str] = field(default_factory=list)
sanitized: str | None = None
advisory: bool = False
class OutputScanner:
"""Post-check: scan final answer for policy violations, PII leakage."""
def __init__(self, mode: GovernanceMode = GovernanceMode.ADVISORY) -> None:
self._pii_patterns: list[tuple[str, re.Pattern[str]]] = [
("ssn", re.compile(r"\b\d{3}-\d{2}-\d{4}\b")),
("credit_card", re.compile(r"\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b")),
]
self._blocked_patterns: list[re.Pattern[str]] = []
self._mode = mode
def add_pii_pattern(self, name: str, pattern: str) -> None:
"""Add PII detection pattern."""
self._pii_patterns.append((name, re.compile(pattern)))
def add_blocked_pattern(self, pattern: str) -> None:
"""Add pattern that flags (advisory) or fails (enforcing) the output."""
self._blocked_patterns.append(re.compile(pattern, re.I))
def scan(self, text: str) -> OutputScanResult:
"""Scan output; return result based on governance mode."""
flags: list[str] = []
for name, pat in self._pii_patterns:
if pat.search(text):
flags.append(f"potential_pii:{name}")
for pat in self._blocked_patterns:
if pat.search(text):
flags.append("blocked_content_detected")
if flags:
if self._mode == GovernanceMode.ADVISORY:
logger.info(
"Output advisory: flags detected (proceeding)",
extra={"flags": flags, "mode": "advisory"},
)
return OutputScanResult(passed=True, flags=flags, advisory=True)
return OutputScanResult(passed=False, flags=flags)
return OutputScanResult(passed=True, flags=[])
class SafetyPipeline:
"""Combined pre/post safety checks for Dvādaśa.
In ADVISORY mode (default), all checks produce logged advisories
instead of hard blocks. The system learns from the outcomes.
"""
def __init__(
self,
moderator: InputModerator | None = None,
scanner: OutputScanner | None = None,
guardrails: Guardrails | None = None,
audit_log: Any | None = None,
mode: GovernanceMode = GovernanceMode.ADVISORY,
) -> None:
self._mode = mode
self._moderator = moderator or InputModerator(mode=mode)
self._scanner = scanner or OutputScanner(mode=mode)
self._guardrails = guardrails or Guardrails(mode=mode)
self._audit = audit_log
@property
def mode(self) -> GovernanceMode:
"""Current governance mode."""
return self._mode
@mode.setter
def mode(self, value: GovernanceMode) -> None:
"""Switch governance mode at runtime."""
self._mode = value
self._moderator._mode = value
self._scanner._mode = value
self._guardrails._mode = value
logger.info("SafetyPipeline mode changed", extra={"mode": value.value})
def pre_check(self, user_input: str) -> ModerationResult:
"""Run input moderation."""
result = self._moderator.moderate(user_input)
if self._audit:
if result.advisory:
self._audit.append(
AuditEventType.ADVISORY,
actor="safety_pipeline",
action="input_moderation_advisory",
payload={"reason": result.reason, "input_preview": user_input[:100]},
outcome="advised_proceed",
)
elif not result.allowed:
self._audit.append(
AuditEventType.POLICY_CHECK,
actor="safety_pipeline",
action="input_moderation",
payload={"reason": result.reason},
outcome="denied",
)
return result
def post_check(self, final_answer: str) -> OutputScanResult:
"""Run output scan."""
result = self._scanner.scan(final_answer)
if self._audit:
if result.advisory:
self._audit.append(
AuditEventType.ADVISORY,
actor="safety_pipeline",
action="output_scan_advisory",
payload={"flags": result.flags, "output_preview": final_answer[:100]},
outcome="advised_proceed",
)
elif not result.passed:
self._audit.append(
AuditEventType.POLICY_CHECK,
actor="safety_pipeline",
action="output_scan",
payload={"flags": result.flags},
outcome="flagged",
)
return result