Anthropic-Cybersecurity-Skills/skills/performing-indicator-lifecycle-management/SKILL.md

name, description, domain, subdomain, tags, version, author, license, nist_csf, mitre_attack

name	description	domain	subdomain	tags	version	author	license	nist_csf	mitre_attack
performing-indicator-lifecycle-management	Tracks IOCs through discovery, enrichment/validation (VirusTotal, Shodan, passive DNS), deployment to SIEM/IDS watchlists, hit-rate and false-positive monitoring, confidence-score decay, and automated expiration using MISP/OpenCTI and STIX. Use when building or maintaining a threat intelligence indicator lifecycle process, aging out stale IOCs, or reducing analyst fatigue from low-quality indicators.	cybersecurity	threat-intelligence	threat-intelligence cti ioc mitre-attack stix indicator-lifecycle ioc-management	1.0	mahipal	Apache-2.0	ID.RA-01 ID.RA-05 DE.CM-01 DE.AE-02	T1591 T1592 T1593 T1589

Performing Indicator Lifecycle Management

Overview

Indicator lifecycle management tracks IOCs from initial discovery through validation, enrichment, deployment, monitoring, and eventual retirement. This skill covers implementing systematic processes for IOC quality assessment, aging policies, confidence scoring decay, false positive tracking, hit-rate monitoring, and automated expiration to maintain a high-quality, actionable indicator database that minimizes analyst fatigue and maximizes detection efficacy.

When to Use

• When conducting security assessments that involve performing indicator lifecycle management
• When following incident response procedures for related security events
• When performing scheduled security testing or auditing activities
• When validating security controls through hands-on testing

Prerequisites

• Python 3.9+ with pymisp, requests, stix2 libraries
• MISP or OpenCTI instance for indicator storage
• SIEM with IOC watchlist capabilities (Splunk, Elastic)
• Understanding of IOC types, confidence scoring, and TLP classifications

Key Concepts

Indicator Lifecycle Phases

1. Discovery: IOC first identified from threat intelligence, malware analysis, or incident response
2. Validation: IOC verified against enrichment sources (VirusTotal, Shodan)
3. Enrichment: Additional context added (WHOIS, passive DNS, threat actor attribution)
4. Deployment: IOC pushed to detection systems (SIEM, IDS, firewall)
5. Monitoring: Track hit rates, false positive rates, detection efficacy
6. Review: Periodic assessment of IOC relevance and accuracy
7. Retirement: IOC expired or removed based on aging policy

Confidence Decay

Indicator confidence decreases over time as adversaries rotate infrastructure. A time-based decay function reduces confidence scores automatically, ensuring old indicators do not generate excessive alerts. Typical half-life: IP addresses (30 days), domains (90 days), file hashes (365 days).

Quality Metrics

• Hit Rate: Percentage of deployed IOCs generating true positive alerts
• False Positive Rate: Percentage of IOC alerts that are benign
• Coverage: Percentage of known threat techniques with IOC coverage
• Freshness: Average age of active indicators in the database

Workflow

Step 1: Implement IOC Lifecycle State Machine

from datetime import datetime, timedelta
from enum import Enum

class IOCState(Enum):
    DISCOVERED = "discovered"
    VALIDATED = "validated"
    ENRICHED = "enriched"
    DEPLOYED = "deployed"
    MONITORING = "monitoring"
    UNDER_REVIEW = "under_review"
    RETIRED = "retired"

class IOCLifecycle:
    def __init__(self, ioc_type, value, source, initial_confidence=50):
        self.ioc_type = ioc_type
        self.value = value
        self.source = source
        self.confidence = initial_confidence
        self.state = IOCState.DISCOVERED
        self.created = datetime.utcnow()
        self.last_updated = datetime.utcnow()
        self.last_seen = None
        self.hit_count = 0
        self.false_positive_count = 0
        self.history = [{"state": "discovered", "timestamp": self.created.isoformat()}]

    def transition(self, new_state: IOCState, reason=""):
        self.state = new_state
        self.last_updated = datetime.utcnow()
        self.history.append({
            "state": new_state.value,
            "timestamp": self.last_updated.isoformat(),
            "reason": reason,
        })

    def apply_decay(self):
        """Apply confidence decay based on IOC type half-life."""
        half_lives = {"ip": 30, "domain": 90, "hash": 365, "url": 60}
        half_life = half_lives.get(self.ioc_type, 90)
        age_days = (datetime.utcnow() - self.created).days
        decay_factor = 0.5 ** (age_days / half_life)
        self.confidence = max(0, int(self.confidence * decay_factor))

    def record_hit(self, is_true_positive=True):
        self.hit_count += 1
        self.last_seen = datetime.utcnow()
        if not is_true_positive:
            self.false_positive_count += 1
            if self.false_positive_count > 3:
                self.transition(IOCState.UNDER_REVIEW, "Excessive false positives")

    def should_retire(self):
        max_ages = {"ip": 90, "domain": 180, "hash": 730, "url": 120}
        max_age = max_ages.get(self.ioc_type, 180)
        age_days = (datetime.utcnow() - self.created).days
        return age_days > max_age and self.hit_count == 0

Validation Criteria

• IOC lifecycle state machine transitions correctly between phases
• Confidence decay reduces scores based on IOC type half-life
• Hit rate and false positive tracking functional
• Aging policy automatically flags indicators for review/retirement
• Quality metrics dashboard shows IOC database health

References

• MISP Indicator Lifecycle
• STIX Indicator Valid From/Until
• IOC Quality Framework