{
  "$type": "site.standard.document",
  "bskyPostRef": {
    "cid": "bafyreiffqkjisqkb3uka5u2k2asixt5wt64jweak3etgxt3wmxsaheyy3u",
    "uri": "at://did:plc:2ikdxjcpbsuoe6mv3qawmazg/app.bsky.feed.post/3mo2giryc2hn2"
  },
  "coverImage": {
    "$type": "blob",
    "ref": {
      "$link": "bafkreigy7lu7xv2frbkrtcmtzfk37r6hpvfnnpf3ckgnzp2ydd623sbpai"
    },
    "mimeType": "image/png",
    "size": 174388
  },
  "path": "/security/making-secret-scanning-more-trustworthy-reducing-false-positives-at-scale/",
  "publishedAt": "2026-06-11T16:00:00.000Z",
  "site": "https://github.blog",
  "tags": [
    "AI & ML",
    "LLMs",
    "Security",
    "Secret Scanning",
    "Making secret scanning more trustworthy: Reducing false positives at scale",
    "The GitHub Blog"
  ],
  "textContent": "Alerts are more trustworthy and actionable when noise is reduced. See how we improved the verification step with context-aware LLM reasoning.\n\nThe post Making secret scanning more trustworthy: Reducing false positives at scale appeared first on The GitHub Blog.",
  "title": "Making secret scanning more trustworthy: Reducing false positives at scale"
}