[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFECCNDBGMT1C9JJBG6D4T":3,"skill-same:01M6AFECCNDBGMT1C9JJBG6D4T":99},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":61,"repositoryHtmlUrl":62,"repositoryHomepageUrl":63,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M6AFECCNDBGMT1C9JJBG6D4T","implementing-threat-intelligence-lifecycle-management",null,"Build out a full CTI program around the six-phase threat intelligence lifecycle (direction, collection, processing, analysis, dissemination, feedback), including defining intelligence requirements, building a collection pipeline, normalizing data, and tracking dissemination feedback. Use when standing up or maturing a threat intelligence program, defining intelligence requirements, or designing collection-to-dissemination workflows for a CTI team.","mukul975\u002FAnthropic-Cybersecurity-Skills","mukul975","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F42860185?v=4","01M6ADEQR8T5M4EEG6C67BQCWF","Mahipal","01M6ADJPMYWG1E5GM1FWBE3128",35990,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-threat-intelligence-lifecycle-management\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"threat intelligence","lifecycle","implementing threat intelligence lifecycle management","cybersecurity","workflows","threat-intelligence","cti","data","pipeline","CTI-program","intelligence-cycle","collection","analysis","dissemination","strategic-intelligence","2026-11-10 10:12:30",false,"---\nname: implementing-threat-intelligence-lifecycle-management\ndescription: Build out a full CTI program around the six-phase threat intelligence\n  lifecycle (direction, collection, processing, analysis, dissemination, feedback),\n  including defining intelligence requirements, building a collection pipeline, normalizing\n  data, and tracking dissemination feedback. Use when standing up or maturing a threat\n  intelligence program, defining intelligence requirements, or designing collection-to-dissemination\n  workflows for a CTI team.\ndomain: cybersecurity\nsubdomain: threat-intelligence\ntags:\n- threat-intelligence\n- lifecycle\n- intelligence-cycle\n- collection\n- analysis\n- dissemination\n- strategic-intelligence\n- cti-program\nversion: '1.0'\nauthor: mahipal\nlicense: Apache-2.0\nnist_csf:\n- ID.RA-01\n- ID.RA-05\n- DE.CM-01\n- DE.AE-02\nmitre_attack:\n- T1591\n- T1592\n- T1593\n- T1589\n---\n# Implementing Threat Intelligence Lifecycle Management\n\n## Overview\n\nThe threat intelligence lifecycle is a structured, iterative process for transforming raw data into actionable intelligence. Based on the intelligence cycle used by military and government agencies, it comprises six phases: Direction (requirements gathering), Collection (data acquisition), Processing (normalization and deduplication), Analysis (contextualization and assessment), Dissemination (distribution to stakeholders), and Feedback (evaluation and refinement). This skill covers building each phase with tooling, metrics, and integration points for a mature CTI program.\n\n\n## When to Use\n\n- When deploying or configuring implementing threat intelligence lifecycle management capabilities in your environment\n- When establishing security controls aligned to compliance requirements\n- When building or improving security architecture for this domain\n- When conducting security assessments that require this implementation\n\n## Prerequisites\n\n- Python 3.9+ with `pymisp`, `stix2`, `requests`, `pandas` libraries\n- MISP or OpenCTI as threat intelligence platform\n- Ticketing system (Jira, ServiceNow) for requirements management\n- SIEM integration (Splunk, Elastic) for indicator operationalization\n- Understanding of intelligence analysis techniques (ACH, Diamond Model)\n\n## Key Concepts\n\n### Intelligence Requirements (IR)\n\nPriority Intelligence Requirements (PIRs) define what the organization needs to know. Examples: Which threat actors target our sector? What vulnerabilities are being actively exploited? Are our brand or credentials being traded on dark web? PIRs drive collection planning and ensure intelligence production is relevant.\n\n### Collection Management Framework\n\nA collection management framework maps intelligence requirements to collection sources, tracks collection gaps, and ensures coverage across the threat landscape. Sources include OSINT, commercial feeds, ISAC sharing, internal telemetry, and human intelligence from industry contacts.\n\n### Intelligence Levels\n\nStrategic intelligence informs executive decision-making (threat landscape, risk trends, geopolitical context). Operational intelligence supports security operations (campaign tracking, actor TTPs, attack timing). Tactical intelligence enables immediate defense (IOCs, detection rules, blocklists).\n\n## Workflow\n\n### Step 1: Define Intelligence Requirements\n\n```python\nimport json\nfrom datetime import datetime\nfrom enum import Enum\n\nclass Priority(Enum):\n    CRITICAL = 1\n    HIGH = 2\n    MEDIUM = 3\n    LOW = 4\n\nclass IntelligenceRequirement:\n    def __init__(self, requirement_id, question, priority, stakeholder,\n                 intelligence_level, collection_sources=None):\n        self.id = requirement_id\n        self.question = question\n        self.priority = priority\n        self.stakeholder = stakeholder\n        self.level = intelligence_level\n        self.sources = collection_sources or []\n        self.created = datetime.now().isoformat()\n        self.status = \"active\"\n        self.last_answered = None\n\n    def to_dict(self):\n        return {\n            \"id\": self.id,\n            \"question\": self.question,\n            \"priority\": self.priority.name,\n            \"stakeholder\": self.stakeholder,\n            \"intelligence_level\": self.level,\n            \"collection_sources\": self.sources,\n            \"created\": self.created,\n            \"status\": self.status,\n            \"last_answered\": self.last_answered,\n        }\n\nclass RequirementsManager:\n    def __init__(self):\n        self.requirements = []\n\n    def add_requirement(self, requirement):\n        self.requirements.append(requirement)\n        print(f\"[+] Added IR-{requirement.id}: {requirement.question[:60]}...\")\n\n    def get_active_requirements(self, priority=None, level=None):\n        filtered = [r for r in self.requirements if r.status == \"active\"]\n        if priority:\n            filtered = [r for r in filtered if r.priority == priority]\n        if level:\n            filtered = [r for r in filtered if r.level == level]\n        return filtered\n\n    def export_requirements(self, output_file=\"intelligence_requirements.json\"):\n        data = [r.to_dict() for r in self.requirements]\n        with open(output_file, \"w\") as f:\n            json.dump(data, f, indent=2)\n        print(f\"[+] Exported {len(data)} requirements to {output_file}\")\n\n# Define organizational PIRs\nmgr = RequirementsManager()\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-001\", \"Which threat actors are actively targeting our sector?\",\n    Priority.CRITICAL, \"CISO\", \"strategic\",\n    [\"MITRE ATT&CK\", \"ISAC feeds\", \"Vendor reports\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-002\", \"What vulnerabilities are being actively exploited in the wild?\",\n    Priority.CRITICAL, \"Vulnerability Management\", \"operational\",\n    [\"CISA KEV\", \"Exploit-DB\", \"VulnCheck\", \"Shodan\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-003\", \"Are any organization credentials or data exposed on dark web?\",\n    Priority.HIGH, \"SOC Manager\", \"tactical\",\n    [\"Dark web monitoring\", \"Paste site monitoring\", \"Breach databases\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-004\", \"What are the emerging attack techniques against cloud infrastructure?\",\n    Priority.HIGH, \"Cloud Security\", \"operational\",\n    [\"ATT&CK Cloud matrix\", \"Vendor advisories\", \"ISAC bulletins\"],\n))\nmgr.export_requirements()\n```\n\n### Step 2: Build Collection Pipeline\n\n```python\nimport requests\nfrom datetime import datetime, timedelta\n\nclass CollectionPipeline:\n    def __init__(self, config):\n        self.config = config\n        self.collected_data = []\n\n    def collect_cisa_kev(self):\n        \"\"\"Collect CISA Known Exploited Vulnerabilities catalog.\"\"\"\n        url = \"https:\u002F\u002Fwww.cisa.gov\u002Fsites\u002Fdefault\u002Ffiles\u002Ffeeds\u002Fknown_exploited_vulnerabilities.json\"\n        resp = requests.get(url, timeout=30)\n        if resp.status_code == 200:\n            data = resp.json()\n            vulns = data.get(\"vulnerabilities\", [])\n            self.collected_data.append({\n                \"source\": \"CISA KEV\",\n                \"type\": \"vulnerability\",\n                \"count\": len(vulns),\n                \"collected_at\": datetime.now().isoformat(),\n                \"data\": vulns,\n            })\n            print(f\"[+] CISA KEV: {len(vulns)} known exploited vulnerabilities\")\n            return vulns\n        return []\n\n    def collect_otx_pulses(self, api_key, days=7):\n        \"\"\"Collect recent OTX pulses.\"\"\"\n        headers = {\"X-OTX-API-KEY\": api_key}\n        since = (datetime.now() - timedelta(days=days)).isoformat()\n        url = f\"https:\u002F\u002Fotx.alienvault.com\u002Fapi\u002Fv1\u002Fpulses\u002Fsubscribed?modified_since={since}\"\n        resp = requests.get(url, headers=headers, timeout=30)\n        if resp.status_code == 200:\n            pulses = resp.json().get(\"results\", [])\n            self.collected_data.append({\n                \"source\": \"AlienVault OTX\",\n                \"type\": \"threat_intelligence\",\n                \"count\": len(pulses),\n                \"collected_at\": datetime.now().isoformat(),\n            })\n            print(f\"[+] OTX: {len(pulses)} pulses in last {days} days\")\n            return pulses\n        return []\n\n    def collect_abuse_ch(self):\n        \"\"\"Collect recent malware samples from MalwareBazaar.\"\"\"\n        url = \"https:\u002F\u002Fmb-api.abuse.ch\u002Fapi\u002Fv1\u002F\"\n        resp = requests.post(url, data={\"query\": \"get_recent\", \"selector\": \"time\"}, timeout=30)\n        if resp.status_code == 200:\n            data = resp.json().get(\"data\", [])\n            self.collected_data.append({\n                \"source\": \"MalwareBazaar\",\n                \"type\": \"malware_samples\",\n                \"count\": len(data),\n                \"collected_at\": datetime.now().isoformat(),\n            })\n            print(f\"[+] MalwareBazaar: {len(data)} recent samples\")\n            return data\n        return []\n\n    def get_collection_summary(self):\n        summary = {\n            \"total_sources\": len(self.collected_data),\n            \"total_items\": sum(d.get(\"count\", 0) for d in self.collected_data),\n            \"sources\": [\n                {\"name\": d[\"source\"], \"type\": d[\"type\"], \"count\": d[\"count\"]}\n                for d in self.collected_data\n            ],\n        }\n        return summary\n\npipeline = CollectionPipeline({})\npipeline.collect_cisa_kev()\npipeline.collect_abuse_ch()\nprint(json.dumps(pipeline.get_collection_summary(), indent=2))\n```\n\n### Step 3: Process and Normalize Data\n\n```python\nclass IntelligenceProcessor:\n    def __init__(self):\n        self.processed_items = []\n        self.dedup_hashes = set()\n\n    def process_collection(self, raw_data, source_name):\n        \"\"\"Normalize and deduplicate collected intelligence.\"\"\"\n        processed = []\n        duplicates = 0\n\n        for item in raw_data:\n            normalized = self._normalize(item, source_name)\n            if normalized:\n                item_hash = self._compute_hash(normalized)\n                if item_hash not in self.dedup_hashes:\n                    self.dedup_hashes.add(item_hash)\n                    normalized[\"processed_at\"] = datetime.now().isoformat()\n                    processed.append(normalized)\n                else:\n                    duplicates += 1\n\n        self.processed_items.extend(processed)\n        print(f\"[+] Processed {len(processed)} items from {source_name} \"\n              f\"({duplicates} duplicates removed)\")\n        return processed\n\n    def _normalize(self, item, source):\n        \"\"\"Normalize item to standard format.\"\"\"\n        return {\n            \"source\": source,\n            \"type\": item.get(\"type\", \"unknown\"),\n            \"value\": item.get(\"value\", item.get(\"indicator\", \"\")),\n            \"confidence\": item.get(\"confidence\", 50),\n            \"tlp\": item.get(\"tlp\", \"green\"),\n            \"tags\": item.get(\"tags\", []),\n            \"first_seen\": item.get(\"first_seen\", item.get(\"date_added\", \"\")),\n            \"raw\": item,\n        }\n\n    def _compute_hash(self, item):\n        import hashlib\n        key = f\"{item['type']}:{item['value']}:{item['source']}\"\n        return hashlib.sha256(key.encode()).hexdigest()\n\nprocessor = IntelligenceProcessor()\n```\n\n### Step 4: Analyze and Produce Intelligence\n\n```python\nclass IntelligenceAnalyzer:\n    def __init__(self, requirements, processed_data):\n        self.requirements = requirements\n        self.data = processed_data\n\n    def answer_requirement(self, requirement_id):\n        \"\"\"Produce intelligence answering a specific requirement.\"\"\"\n        req = next((r for r in self.requirements if r.id == requirement_id), None)\n        if not req:\n            return None\n\n        # Filter relevant data based on requirement type\n        relevant = self.data  # In practice, filter by requirement topic\n        analysis = {\n            \"requirement_id\": requirement_id,\n            \"question\": req.question,\n            \"intelligence_level\": req.level,\n            \"data_points_analyzed\": len(relevant),\n            \"produced_at\": datetime.now().isoformat(),\n            \"key_findings\": [],\n            \"confidence\": \"medium\",\n            \"recommendations\": [],\n        }\n        return analysis\n\n    def produce_daily_brief(self):\n        \"\"\"Produce daily threat intelligence brief.\"\"\"\n        brief = {\n            \"date\": datetime.now().strftime(\"%Y-%m-%d\"),\n            \"total_items_processed\": len(self.data),\n            \"highlights\": [],\n            \"active_requirements_status\": [\n                {\"id\": r.id, \"question\": r.question[:80], \"status\": r.status}\n                for r in self.requirements if r.status == \"active\"\n            ],\n        }\n        return brief\n```\n\n### Step 5: Disseminate and Track Feedback\n\n```python\nclass IntelligenceDisseminator:\n    def __init__(self):\n        self.distribution_log = []\n\n    def distribute_report(self, report, channels, classification=\"TLP:GREEN\"):\n        \"\"\"Distribute intelligence report to appropriate channels.\"\"\"\n        for channel in channels:\n            entry = {\n                \"report_id\": report.get(\"requirement_id\", \"daily\"),\n                \"channel\": channel,\n                \"classification\": classification,\n                \"distributed_at\": datetime.now().isoformat(),\n                \"status\": \"sent\",\n            }\n            self.distribution_log.append(entry)\n            print(f\"  [+] Distributed to {channel}\")\n\n    def collect_feedback(self, report_id, stakeholder, rating, comments=\"\"):\n        \"\"\"Collect stakeholder feedback on intelligence product.\"\"\"\n        feedback = {\n            \"report_id\": report_id,\n            \"stakeholder\": stakeholder,\n            \"rating\": rating,  # 1-5\n            \"comments\": comments,\n            \"received_at\": datetime.now().isoformat(),\n        }\n        print(f\"[+] Feedback received from {stakeholder}: {rating}\u002F5\")\n        return feedback\n\n    def calculate_metrics(self):\n        \"\"\"Calculate CTI program performance metrics.\"\"\"\n        metrics = {\n            \"total_products_distributed\": len(self.distribution_log),\n            \"distribution_by_channel\": {},\n        }\n        for entry in self.distribution_log:\n            channel = entry[\"channel\"]\n            if channel not in metrics[\"distribution_by_channel\"]:\n                metrics[\"distribution_by_channel\"][channel] = 0\n            metrics[\"distribution_by_channel\"][channel] += 1\n        return metrics\n\ndisseminator = IntelligenceDisseminator()\n```\n\n## Validation Criteria\n\n- Intelligence requirements defined with priorities and stakeholders\n- Collection pipeline gathering from multiple sources\n- Processing deduplicates and normalizes data correctly\n- Analysis produces intelligence answering specific requirements\n- Dissemination reaches appropriate stakeholders through right channels\n- Feedback mechanism captures and incorporates stakeholder input\n\n## References\n\n- [SANS: Cyber Threat Intelligence Lifecycle](https:\u002F\u002Fwww.sans.org\u002Fwhite-papers\u002F36297\u002F)\n- [CISA: Cybersecurity Automation Best Practices](https:\u002F\u002Fwww.cisa.gov\u002Fsites\u002Fdefault\u002Ffiles\u002Fpublications\u002FOperational%20Value%20of%20IOCs_508c.pdf)\n- [CyCognito: Threat Intelligence Lifecycle](https:\u002F\u002Fwww.cycognito.com\u002Flearn\u002Fthreat-intelligence\u002F)\n- [MISP Project](https:\u002F\u002Fwww.misp-project.org\u002F)\n- [STIX\u002FTAXII Documentation](https:\u002F\u002Foasis-open.github.io\u002Fcti-documentation\u002F)\n- [CISA Known Exploited Vulnerabilities](https:\u002F\u002Fwww.cisa.gov\u002Fknown-exploited-vulnerabilities-catalog)\n","# Implementing Threat Intelligence Lifecycle Management\n\n## Overview\n\nThe threat intelligence lifecycle is a structured, iterative process for transforming raw data into actionable intelligence. Based on the intelligence cycle used by military and government agencies, it comprises six phases: Direction (requirements gathering), Collection (data acquisition), Processing (normalization and deduplication), Analysis (contextualization and assessment), Dissemination (distribution to stakeholders), and Feedback (evaluation and refinement). This skill covers building each phase with tooling, metrics, and integration points for a mature CTI program.\n\n\n## When to Use\n\n- When deploying or configuring implementing threat intelligence lifecycle management capabilities in your environment\n- When establishing security controls aligned to compliance requirements\n- When building or improving security architecture for this domain\n- When conducting security assessments that require this implementation\n\n## Prerequisites\n\n- Python 3.9+ with `pymisp`, `stix2`, `requests`, `pandas` libraries\n- MISP or OpenCTI as threat intelligence platform\n- Ticketing system (Jira, ServiceNow) for requirements management\n- SIEM integration (Splunk, Elastic) for indicator operationalization\n- Understanding of intelligence analysis techniques (ACH, Diamond Model)\n\n## Key Concepts\n\n### Intelligence Requirements (IR)\n\nPriority Intelligence Requirements (PIRs) define what the organization needs to know. Examples: Which threat actors target our sector? What vulnerabilities are being actively exploited? Are our brand or credentials being traded on dark web? PIRs drive collection planning and ensure intelligence production is relevant.\n\n### Collection Management Framework\n\nA collection management framework maps intelligence requirements to collection sources, tracks collection gaps, and ensures coverage across the threat landscape. Sources include OSINT, commercial feeds, ISAC sharing, internal telemetry, and human intelligence from industry contacts.\n\n### Intelligence Levels\n\nStrategic intelligence informs executive decision-making (threat landscape, risk trends, geopolitical context). Operational intelligence supports security operations (campaign tracking, actor TTPs, attack timing). Tactical intelligence enables immediate defense (IOCs, detection rules, blocklists).\n\n## Workflow\n\n### Step 1: Define Intelligence Requirements\n\n```python\nimport json\nfrom datetime import datetime\nfrom enum import Enum\n\nclass Priority(Enum):\n    CRITICAL = 1\n    HIGH = 2\n    MEDIUM = 3\n    LOW = 4\n\nclass IntelligenceRequirement:\n    def __init__(self, requirement_id, question, priority, stakeholder,\n                 intelligence_level, collection_sources=None):\n        self.id = requirement_id\n        self.question = question\n        self.priority = priority\n        self.stakeholder = stakeholder\n        self.level = intelligence_level\n        self.sources = collection_sources or []\n        self.created = datetime.now().isoformat()\n        self.status = \"active\"\n        self.last_answered = None\n\n    def to_dict(self):\n        return {\n            \"id\": self.id,\n            \"question\": self.question,\n            \"priority\": self.priority.name,\n            \"stakeholder\": self.stakeholder,\n            \"intelligence_level\": self.level,\n            \"collection_sources\": self.sources,\n            \"created\": self.created,\n            \"status\": self.status,\n            \"last_answered\": self.last_answered,\n        }\n\nclass RequirementsManager:\n    def __init__(self):\n        self.requirements = []\n\n    def add_requirement(self, requirement):\n        self.requirements.append(requirement)\n        print(f\"[+] Added IR-{requirement.id}: {requirement.question[:60]}...\")\n\n    def get_active_requirements(self, priority=None, level=None):\n        filtered = [r for r in self.requirements if r.status == \"active\"]\n        if priority:\n            filtered = [r for r in filtered if r.priority == priority]\n        if level:\n            filtered = [r for r in filtered if r.level == level]\n        return filtered\n\n    def export_requirements(self, output_file=\"intelligence_requirements.json\"):\n        data = [r.to_dict() for r in self.requirements]\n        with open(output_file, \"w\") as f:\n            json.dump(data, f, indent=2)\n        print(f\"[+] Exported {len(data)} requirements to {output_file}\")\n\n# Define organizational PIRs\nmgr = RequirementsManager()\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-001\", \"Which threat actors are actively targeting our sector?\",\n    Priority.CRITICAL, \"CISO\", \"strategic\",\n    [\"MITRE ATT&CK\", \"ISAC feeds\", \"Vendor reports\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-002\", \"What vulnerabilities are being actively exploited in the wild?\",\n    Priority.CRITICAL, \"Vulnerability Management\", \"operational\",\n    [\"CISA KEV\", \"Exploit-DB\", \"VulnCheck\", \"Shodan\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-003\", \"Are any organization credentials or data exposed on dark web?\",\n    Priority.HIGH, \"SOC Manager\", \"tactical\",\n    [\"Dark web monitoring\", \"Paste site monitoring\", \"Breach databases\"],\n))\nmgr.add_requirement(IntelligenceRequirement(\n    \"PIR-004\", \"What are the emerging attack techniques against cloud infrastructure?\",\n    Priority.HIGH, \"Cloud Security\", \"operational\",\n    [\"ATT&CK Cloud matrix\", \"Vendor advisories\", \"ISAC bulletins\"],\n))\nmgr.export_requirements()\n```\n\n### Step 2: Build Collection Pipeline\n\n```python\nimport requests\nfrom datetime import datetime, timedelta\n\nclass CollectionPipeline:\n    def __init__(self, config):\n        self.config = config\n        self.collected_data = []\n\n    def collect_cisa_kev(self):\n        \"\"\"Collect CISA Known Exploited Vulnerabilities catalog.\"\"\"\n        url = \"https:\u002F\u002Fwww.cisa.gov\u002Fsites\u002Fdefault\u002Ffiles\u002Ffeeds\u002Fknown_exploited_vulnerabilities.json\"\n        resp = requests.get(url, timeout=30)\n        if resp.status_code == 200:\n            data = resp.json()\n            vulns = data.get(\"vulnerabilities\", [])\n            self.collected_data.append({\n                \"source\": \"CISA KEV\",\n                \"type\": \"vulnerability\",\n                \"count\": len(vulns),\n                \"collected_at\": datetime.now().isoformat(),\n                \"data\": vulns,\n            })\n            print(f\"[+] CISA KEV: {len(vulns)} known exploited vulnerabilities\")\n            return vulns\n        return []\n\n    def collect_otx_pulses(self, api_key, days=7):\n        \"\"\"Collect recent OTX pulses.\"\"\"\n        headers = {\"X-OTX-API-KEY\": api_key}\n        since = (datetime.now() - timedelta(days=days)).isoformat()\n        url = f\"https:\u002F\u002Fotx.alienvault.com\u002Fapi\u002Fv1\u002Fpulses\u002Fsubscribed?modified_since={since}\"\n        resp = requests.get(url, headers=headers, timeout=30)\n        if resp.status_code == 200:\n            pulses = resp.json().get(\"results\", [])\n            self.collected_data.append({\n                \"source\": \"AlienVault OTX\",\n                \"type\": \"threat_intelligence\",\n                \"count\": len(pulses),\n                \"collected_at\": datetime.now().isoformat(),\n            })\n            print(f\"[+] OTX: {len(pulses)} pulses in last {days} days\")\n            return pulses\n        return []\n\n    def collect_abuse_ch(self):\n        \"\"\"Collect recent malware samples from MalwareBazaar.\"\"\"\n        url = \"https:\u002F\u002Fmb-api.abuse.ch\u002Fapi\u002Fv1\u002F\"\n        resp = requests.post(url, data={\"query\": \"get_recent\", \"selector\": \"time\"}, timeout=30)\n        if resp.status_code == 200:\n            data = resp.json().get(\"data\", [])\n            self.collected_data.append({\n                \"source\": \"MalwareBazaar\",\n                \"type\": \"malware_samples\",\n                \"count\": len(data),\n                \"collected_at\": datetime.now().isoformat(),\n            })\n            print(f\"[+] MalwareBazaar: {len(data)} recent samples\")\n            return data\n        return []\n\n    def get_collection_summary(self):\n        summary = {\n            \"total_sources\": len(self.collected_data),\n            \"total_items\": sum(d.get(\"count\", 0) for d in self.collected_data),\n            \"sources\": [\n                {\"name\": d[\"source\"], \"type\": d[\"type\"], \"count\": d[\"count\"]}\n                for d in self.collected_data\n            ],\n        }\n        return summary\n\npipeline = CollectionPipeline({})\npipeline.collect_cisa_kev()\npipeline.collect_abuse_ch()\nprint(json.dumps(pipeline.get_collection_summary(), indent=2))\n```\n\n### Step 3: Process and Normalize Data\n\n```python\nclass IntelligenceProcessor:\n    def __init__(self):\n        self.processed_items = []\n        self.dedup_hashes = set()\n\n    def process_collection(self, raw_data, source_name):\n        \"\"\"Normalize and deduplicate collected intelligence.\"\"\"\n        processed = []\n        duplicates = 0\n\n        for item in raw_data:\n            normalized = self._normalize(item, source_name)\n            if normalized:\n                item_hash = self._compute_hash(normalized)\n                if item_hash not in self.dedup_hashes:\n                    self.dedup_hashes.add(item_hash)\n                    normalized[\"processed_at\"] = datetime.now().isoformat()\n                    processed.append(normalized)\n                else:\n                    duplicates += 1\n\n        self.processed_items.extend(processed)\n        print(f\"[+] Processed {len(processed)} items from {source_name} \"\n              f\"({duplicates} duplicates removed)\")\n        return processed\n\n    def _normalize(self, item, source):\n        \"\"\"Normalize item to standard format.\"\"\"\n        return {\n            \"source\": source,\n            \"type\": item.get(\"type\", \"unknown\"),\n            \"value\": item.get(\"value\", item.get(\"indicator\", \"\")),\n            \"confidence\": item.get(\"confidence\", 50),\n            \"tlp\": item.get(\"tlp\", \"green\"),\n            \"tags\": item.get(\"tags\", []),\n            \"first_seen\": item.get(\"first_seen\", item.get(\"date_added\", \"\")),\n            \"raw\": item,\n        }\n\n    def _compute_hash(self, item):\n        import hashlib\n        key = f\"{item['type']}:{item['value']}:{item['source']}\"\n        return hashlib.sha256(key.encode()).hexdigest()\n\nprocessor = IntelligenceProcessor()\n```\n\n### Step 4: Analyze and Produce Intelligence\n\n```python\nclass IntelligenceAnalyzer:\n    def __init__(self, requirements, processed_data):\n        self.requirements = requirements\n        self.data = processed_data\n\n    def answer_requirement(self, requirement_id):\n        \"\"\"Produce intelligence answering a specific requirement.\"\"\"\n        req = next((r for r in self.requirements if r.id == requirement_id), None)\n        if not req:\n            return None\n\n        # Filter relevant data based on requirement type\n        relevant = self.data  # In practice, filter by requirement topic\n        analysis = {\n            \"requirement_id\": requirement_id,\n            \"question\": req.question,\n            \"intelligence_level\": req.level,\n            \"data_points_analyzed\": len(relevant),\n            \"produced_at\": datetime.now().isoformat(),\n            \"key_findings\": [],\n            \"confidence\": \"medium\",\n            \"recommendations\": [],\n        }\n        return analysis\n\n    def produce_daily_brief(self):\n        \"\"\"Produce daily threat intelligence brief.\"\"\"\n        brief = {\n            \"date\": datetime.now().strftime(\"%Y-%m-%d\"),\n            \"total_items_processed\": len(self.data),\n            \"highlights\": [],\n            \"active_requirements_status\": [\n                {\"id\": r.id, \"question\": r.question[:80], \"status\": r.status}\n                for r in self.requirements if r.status == \"active\"\n            ],\n        }\n        return brief\n```\n\n### Step 5: Disseminate and Track Feedback\n\n```python\nclass IntelligenceDisseminator:\n    def __init__(self):\n        self.distribution_log = []\n\n    def distribute_report(self, report, channels, classification=\"TLP:GREEN\"):\n        \"\"\"Distribute intelligence report to appropriate channels.\"\"\"\n        for channel in channels:\n            entry = {\n                \"report_id\": report.get(\"requirement_id\", \"daily\"),\n                \"channel\": channel,\n                \"classification\": classification,\n                \"distributed_at\": datetime.now().isoformat(),\n                \"status\": \"sent\",\n            }\n            self.distribution_log.append(entry)\n            print(f\"  [+] Distributed to {channel}\")\n\n    def collect_feedback(self, report_id, stakeholder, rating, comments=\"\"):\n        \"\"\"Collect stakeholder feedback on intelligence product.\"\"\"\n        feedback = {\n            \"report_id\": report_id,\n            \"stakeholder\": stakeholder,\n            \"rating\": rating,  # 1-5\n            \"comments\": comments,\n            \"received_at\": datetime.now().isoformat(),\n        }\n        print(f\"[+] Feedback received from {stakeholder}: {rating}\u002F5\")\n        return feedback\n\n    def calculate_metrics(self):\n        \"\"\"Calculate CTI program performance metrics.\"\"\"\n        metrics = {\n            \"total_products_distributed\": len(self.distribution_log),\n            \"distribution_by_channel\": {},\n        }\n        for entry in self.distribution_log:\n            channel = entry[\"channel\"]\n            if channel not in metrics[\"distribution_by_channel\"]:\n                metrics[\"distribution_by_channel\"][channel] = 0\n            metrics[\"distribution_by_channel\"][channel] += 1\n        return metrics\n\ndisseminator = IntelligenceDisseminator()\n```\n\n## Validation Criteria\n\n- Intelligence requirements defined with priorities and stakeholders\n- Collection pipeline gathering from multiple sources\n- Processing deduplicates and normalizes data correctly\n- Analysis produces intelligence answering specific requirements\n- Dissemination reaches appropriate stakeholders through right channels\n- Feedback mechanism captures and incorporates stakeholder input\n\n## References\n\n- [SANS: Cyber Threat Intelligence Lifecycle](https:\u002F\u002Fwww.sans.org\u002Fwhite-papers\u002F36297\u002F)\n- [CISA: Cybersecurity Automation Best Practices](https:\u002F\u002Fwww.cisa.gov\u002Fsites\u002Fdefault\u002Ffiles\u002Fpublications\u002FOperational%20Value%20of%20IOCs_508c.pdf)\n- [CyCognito: Threat Intelligence Lifecycle](https:\u002F\u002Fwww.cycognito.com\u002Flearn\u002Fthreat-intelligence\u002F)\n- [MISP Project](https:\u002F\u002Fwww.misp-project.org\u002F)\n- [STIX\u002FTAXII Documentation](https:\u002F\u002Foasis-open.github.io\u002Fcti-documentation\u002F)\n- [CISA Known Exploited Vulnerabilities](https:\u002F\u002Fwww.cisa.gov\u002Fknown-exploited-vulnerabilities-catalog)\n",{"name":5,"tags":44,"author":46,"domain":27,"license":15,"version":47,"nist_csf":48,"subdomain":29,"description":7,"mitre_attack":53},[29,25,34,35,36,37,38,45],"cti-program","mahipal","1.0",[49,50,51,52],"ID.RA-01","ID.RA-05","DE.CM-01","DE.AE-02",[54,55,56,57],"T1591","T1592","T1593","T1589","main","54a798831d2266a3ca61ce68a7acb80b81160d57","skills\u002Fimplementing-threat-intelligence-lifecycle-management",4395,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills","https:\u002F\u002Fmahipal.engineer\u002FAnthropic-Cybersecurity-Skills\u002F","https:\u002F\u002Fgithub.com\u002Fmukul975","Mahipal.engineer","🔐 Cybersecurity | 💻 Dev | 📷 Street Photographer\r\n🎓 MSc | 📚 Research & AI Security","2026-08-31 12:32:44",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill implementing-threat-intelligence-lifecycle-management",{"tool":74,"command":75},"bunx","bunx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill implementing-threat-intelligence-lifecycle-management",{"tool":77,"command":78},"pnpm","pnpm dlx skills add mukul975\u002FAnthropic-Cybersecurity-Skills --skill implementing-threat-intelligence-lifecycle-management","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-threat-intelligence-lifecycle-management\u002FSKILL.md",true,[82,87,93],{"path":83,"resourceType":83,"resourceTypeDesc":84,"sizeBytes":85,"sourceHtmlUrl":86},"LICENSE","许可证",11255,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-threat-intelligence-lifecycle-management\u002FLICENSE",{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fapi-reference.md","REFERENCE","参考文档",5005,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-threat-intelligence-lifecycle-management\u002Freferences\u002Fapi-reference.md",{"path":94,"resourceType":95,"resourceTypeDesc":96,"sizeBytes":97,"sourceHtmlUrl":98},"scripts\u002Fagent.py","SCRIPT","脚本",9148,"https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fimplementing-threat-intelligence-lifecycle-management\u002Fscripts\u002Fagent.py",[100,126,150,178,202,229],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":107,"publishedAt":39},"01M6AGDM3HJXTKXX15ZZ0ZVCJ3","verifying-build-provenance-with-slsa-sigstore","Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI\u002FCD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fverifying-build-provenance-with-slsa-sigstore\u002FSKILL.md",[106],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[108,109,110,111,112,27,113,114,115,116,117,118,119,32,120,121,122,123,124,125],"sigstore","provenance","slsa","verifying build provenance with slsa sigstore","security","incident response","identity","pr","code","artifacts","ci","supply-chain","supply-chain-security","t1195","attestation","code-signing","cosign","keyless",{"skillNo":127,"name":128,"nameZh":6,"description":129,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":130,"categories":131,"keywords":133,"publishedAt":39},"01M6AGDGBPWYVZ2F11GFKJ4HZ9","validating-tpm-measured-boot-attestation","Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote\u002Ftpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware\u002Fkernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-tpm-measured-boot-attestation\u002FSKILL.md",[132],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[134,122,135,136,112,137,27,115,138,139,140,141,142,143,144,145,146,147,148,149],"boot","TPM","validating tpm measured boot attestation","policy","verification","ZeRO","firmware","Tools","measured-boot","PCR","integrity","secrets","remote-attestation","tpm2-tools","integrity-verification","trusted-computing",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":160,"publishedAt":39},"01M6AGDCW0Y4FXZ3362GT1941M","validating-backup-integrity-for-recovery","Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable\u002Fair-gapped backups for silent corruption or tampering.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Fvalidating-backup-integrity-for-recovery\u002FSKILL.md",[156,159],{"categoryNo":157,"categoryName":158,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[144,161,162,163,164,27,165,166,138,167,168,169,170,171,172,173,174,175,176,177],"backup","recovery","validating backup integrity for recovery","testing","infrastructure","validation","compliance","hash-verification","incident-response","ransomware","cryptocurrency","detection","t1078","t1490","immutable","disaster-recovery","restore-testing",{"skillNo":179,"name":180,"nameZh":6,"description":181,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":182,"categories":183,"keywords":185,"publishedAt":39},"01M6AGD9GBG2KZ336AE763J3WH","triaging-windows-with-kape","Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment\u002Ftriage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-windows-with-kape\u002FSKILL.md",[184],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[186,187,188,27,189,190,191,192,193,194,195,31,196,197,198,199,141,35,200,201],"windows","kape","triaging windows with kape","registry","browser","logs","windows-forensics","dfir","mft","mftecmd","prefetch","eric-zimmerman","digital-forensics","SET","pecmd","artifact-collection",{"skillNo":203,"name":204,"nameZh":6,"description":205,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":206,"categories":207,"keywords":209,"publishedAt":39},"01M6AGD5PW5J4EVZM958WD74ZQ","triaging-vulnerabilities-with-ssvc-framework","Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track\u002FTrack*\u002FAttend\u002FAct decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-vulnerabilities-with-ssvc-framework\u002FSKILL.md",[208],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[210,211,212,27,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228],"vulnerabilities","framework","triaging vulnerabilities with ssvc framework","status","planning","CISA","vulnerability-management","risk-management","nessus","qualys","openvas","cvss","remediation","risk","vulnerability","api","prioritization","ssvc","decision-tree",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":233,"categories":234,"keywords":236,"publishedAt":39},"01M6AGD24TTQQM4CXG69E3B9TJ","triaging-security-incident","Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM\u002FEDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.","https:\u002F\u002Fgithub.com\u002Fmukul975\u002FAnthropic-Cybersecurity-Skills\u002Fblob\u002Fmain\u002Fskills\u002Ftriaging-security-incident\u002FSKILL.md",[235],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[112,237,27,238,239,240,241,242,243,244,245,173,246,174,36,247,248,249,250,251],"triaging security incident","siem","file","ioc","soc","NIST-800-61","NIST","severity","edr","execution","classification","severity-classification","incident-triage","SANS-PICERL","SOC-operations"]