{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:BIHC63WKVOEG2TJNVYN3D3GE4L","short_pith_number":"pith:BIHC63WK","schema_version":"1.0","canonical_sha256":"0a0e2f6ecaab886d4d2dae1bb1ecc4e2c3e663ffb592e5c465667a6d0d397925","source":{"kind":"arxiv","id":"2503.02065","version":2},"attestation_state":"computed","paper":{"title":"Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CR","authors_text":"Amulya Saxena, Devang Dhanuka, Nidhi Rastogi, Pranjal Mairal, Shirid Pant","submitted_at":"2025-03-03T21:39:15Z","abstract_excerpt":"Explainable AI (XAI) holds significant promise for enhancing the transparency and trustworthiness of AI-driven threat detection in Security Operations Centers (SOCs). However, identifying the appropriate level and format of explanation, particularly in environments that demand rapid decision-making under high-stakes conditions, remains a complex and underexplored challenge. To address this gap, we conducted a three-month mixed-methods study combining an online survey (N1=248) with in-depth interviews (N2=24) to examine (1) how SOC analysts conceptualize AI-generated explanations and (2) which "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2503.02065","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2025-03-03T21:39:15Z","cross_cats_sorted":["cs.AI","cs.IR"],"title_canon_sha256":"9191cefbb2fe8ca8361c7681422b646e6b215368a787ecdd84bfdf8bcb44bbf5","abstract_canon_sha256":"00c7d6822301537f123c8abbaca971310794b5e2f63067ef921826de34d89a4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:50.330966Z","signature_b64":"JES8aE3HUju585zL+gwud3JNw3k5sz0jkasUCxTuG1LLUan+kWdLawT3AlZl41+OnEjhtbnB9WynSvNFRT0/DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a0e2f6ecaab886d4d2dae1bb1ecc4e2c3e663ffb592e5c465667a6d0d397925","last_reissued_at":"2026-07-05T11:39:50.330430Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:50.330430Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Too Much to Trust? Measuring the Security and Cognitive Impacts of Explainability in AI-Driven SOCs","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.IR"],"primary_cat":"cs.CR","authors_text":"Amulya Saxena, Devang Dhanuka, Nidhi Rastogi, Pranjal Mairal, Shirid Pant","submitted_at":"2025-03-03T21:39:15Z","abstract_excerpt":"Explainable AI (XAI) holds significant promise for enhancing the transparency and trustworthiness of AI-driven threat detection in Security Operations Centers (SOCs). However, identifying the appropriate level and format of explanation, particularly in environments that demand rapid decision-making under high-stakes conditions, remains a complex and underexplored challenge. To address this gap, we conducted a three-month mixed-methods study combining an online survey (N1=248) with in-depth interviews (N2=24) to examine (1) how SOC analysts conceptualize AI-generated explanations and (2) which "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.02065","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2503.02065/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2503.02065","created_at":"2026-07-05T11:39:50.330490+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.02065v2","created_at":"2026-07-05T11:39:50.330490+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.02065","created_at":"2026-07-05T11:39:50.330490+00:00"},{"alias_kind":"pith_short_12","alias_value":"BIHC63WKVOEG","created_at":"2026-07-05T11:39:50.330490+00:00"},{"alias_kind":"pith_short_16","alias_value":"BIHC63WKVOEG2TJN","created_at":"2026-07-05T11:39:50.330490+00:00"},{"alias_kind":"pith_short_8","alias_value":"BIHC63WK","created_at":"2026-07-05T11:39:50.330490+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.28929","citing_title":"Cybersecurity is the True Frontier for Generative AI Success or Failure","ref_index":103,"is_internal_anchor":false},{"citing_arxiv_id":"2602.24176","citing_title":"Beyond Explainable AI (XAI): An Overdue Paradigm Shift and Post-XAI Research Directions","ref_index":81,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08316","citing_title":"AI-Driven Security Alert Screening and Alert Fatigue Mitigation in Security Operations Centers: A Comprehensive Survey","ref_index":120,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21679","citing_title":"A Sociotechnical, Practitioner-Centered Approach to Technology Adoption in Cybersecurity Operations: An LLM Case","ref_index":38,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09998","citing_title":"Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit","ref_index":27,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BIHC63WKVOEG2TJNVYN3D3GE4L","json":"https://pith.science/pith/BIHC63WKVOEG2TJNVYN3D3GE4L.json","graph_json":"https://pith.science/api/pith-number/BIHC63WKVOEG2TJNVYN3D3GE4L/graph.json","events_json":"https://pith.science/api/pith-number/BIHC63WKVOEG2TJNVYN3D3GE4L/events.json","paper":"https://pith.science/paper/BIHC63WK"},"agent_actions":{"view_html":"https://pith.science/pith/BIHC63WKVOEG2TJNVYN3D3GE4L","download_json":"https://pith.science/pith/BIHC63WKVOEG2TJNVYN3D3GE4L.json","view_paper":"https://pith.science/paper/BIHC63WK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.02065&json=true","fetch_graph":"https://pith.science/api/pith-number/BIHC63WKVOEG2TJNVYN3D3GE4L/graph.json","fetch_events":"https://pith.science/api/pith-number/BIHC63WKVOEG2TJNVYN3D3GE4L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BIHC63WKVOEG2TJNVYN3D3GE4L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BIHC63WKVOEG2TJNVYN3D3GE4L/action/storage_attestation","attest_author":"https://pith.science/pith/BIHC63WKVOEG2TJNVYN3D3GE4L/action/author_attestation","sign_citation":"https://pith.science/pith/BIHC63WKVOEG2TJNVYN3D3GE4L/action/citation_signature","submit_replication":"https://pith.science/pith/BIHC63WKVOEG2TJNVYN3D3GE4L/action/replication_record"}},"created_at":"2026-07-05T11:39:50.330490+00:00","updated_at":"2026-07-05T11:39:50.330490+00:00"}