Enterprises systematically overestimate their visibility into AI agents (Cloud Security Alliance, Apr 2026: 82% had discovered at least one AI agent running without their security or IT team's knowledge in the past year while 68% believed they had strong visibility, with only 21% running any formal agent decommissioning process), and because a written policy cannot be enforced against agents nobody can see, continuous discovery rather than policy is the binding first control.
Anchored on the CSA + Token Security survey 'Autonomous but Not Controlled: AI Agent Incidents Now Common in Enterprises' (published 21 Apr 2026, n=418 IT/security professionals, fielded Jan 2026): 82% found at least one unknown AI agent in the past year, 68% believe they have strong visibility, 65% had an AI agent security incident, of those incidents 61% involved data exposure, 21% run a formal decommissioning process; Itamar Apelblat (CEO/Co-Founder, Token Security) quote. Scale anchored on CrowdStrike press release (23 Mar 2026): 1,800+ distinct AI applications across ~160 million unique instances, Shadow AI Discovery for Endpoint capability; Michael Sentonas (President) quote. VERIFIED 2026-06-08 via cloudsecurityalliance.org press release/artifact and crowdstrike.com press release. PRECISION: the 61% data-exposure figure is of the incident group (65%), not all 418 respondents — stated as such in the body. Distinct from AM-204 (NHI governance vacuum — machine-identity scale/lifecycle) and AM-168/shadow-ai-discovery-playbook (broad shadow AI): this piece's core is the believed-vs-actual visibility gap and discovery-as-first-control. 90-day cadence. Triggers: (1) a later large-sample survey showing find-rate and believed-visibility converging; (2) discovery tooling becoming a default in major endpoint/cloud platforms; (3) incident data showing policy maturity, not discovery, separates breached from unbreached. Siblings: AM-204 (NHI governance vacuum), the shadow-AI discovery playbook, approved-tool-unapproved-capability.
/holding/AM-205/Embed this claimiframe + oEmbed
The card auto-updates when the claim's status, last-reviewed date, or correction log changes. Embedders never need to refresh — the card is rendered live from the canonical record.
Email-me when AM-205's status, next review date, or correction log changes. One email per change. No newsletter subscription, no other mail.
The claim: Enterprises systematically overestimate their visibility into AI agents (Cloud Security Alliance, Apr 2026: 82% had discovered at least one AI agent running without their security or IT team's knowledge in the past year while 68% believed they had strong visibility, with only 21% running any formal agent decommissioning process), and because a written policy cannot be enforced against agents nobody can see, continuous discovery rather than policy is the binding first control.
About this register
The Reporting register tracks claims published from articles addressed to senior enterprise IT leaders — CIOs, IT directors, heads of platform. Claims are reviewed on a 30–90 day cadence; each review either reaffirms the claim, marks one substantive part as Partial, or marks it Not holding once the underlying evidence has been overtaken.
Recent corrections in Reporting
- AM-008 · Partial · 17 Jun 2026
Source-text figure re-review: Google's 2024 Environmental Report reports a 28% year-over-year increase to 8.1 billion gallons, not the 33% (from a 6.1 billion 2023 base) asserted at publish. The 8.1B 2024 figure and the Microsoft WUE 0.30 L/kWh / 39%-improvement figure are unchanged and verified. Article corrected to 28% and the unsupported 6.1B base removed; the claim text retains the original figure with this correction per the Holding-up protocol.
- AM-132 · Partial · 10 Jun 2026
One of four legs unanchored on re-review. The claim text attributes '12% of deployments clearing 300%+ ROI with 88% at or below break-even at 12-18 months' to the Stanford DEL 2026 Enterprise AI Playbook. Full-text verification on 10 Jun 2026 found no such figure in that source: the playbook (Pereira, Graylin, Brynjolfsson, Apr 2026) studies 51 successful deployments by design and contains no ROI distribution, no 300%-plus cohort, and no break-even measurement point (full finding at AM-029, correction of 10 Jun 2026). The only verified figure carrying the same 12/88 numerals is IDC research with Lenovo (via CIO.com, Mar 2025): roughly 88% of AI proof-of-concepts never reach production and roughly 12% graduate — a pilot-to-production graduation metric, not an ROI distribution. The Gartner 28%, McKinsey 23%/17%, and MIT NANDA 95% legs verify; they support a small high-performing tail and a large struggling body, but none documents the two-peak bimodal shape the claim asserts. Status Up -> Partial.
- AM-129 · Partial · 10 Jun 2026
One of three read-against anchors unanchored on re-review. The claim text cites 'Stanford Digital Economy Lab Enterprise AI Playbook (12/88 bimodal ROI distribution at 12-18 months)' and frames the realistic ROI band around 'the highest-discipline 12% cohort'. Full-text verification on 10 Jun 2026 found the playbook contains no 12/88 distribution, no bimodal ROI shape, and no 12-18-month ROI measurement point (full finding at AM-029, correction of 10 Jun 2026). The claim's core negative finding — no mid-market enterprise has produced a documented +240% ROI in 90 days under audited conditions — is unaffected; the McKinsey State of AI 2025 and MIT NANDA legs verify and continue to support it. The '12% cohort' framing has no verifiable referent. The only verified figure carrying the 12/88 numerals is IDC's pilot-graduation finding (roughly 88% of AI proof-of-concepts never reach production; via CIO.com, Mar 2025), a different metric. Status Up -> Partial.
Reviews coming up in Reporting
- AM-063 · Holding · next +9d (27 Jun 2026)
AI agents executing financial transactions need a four-control bundle (action-approval gates by blast radius, kill-swit…
- AM-061 · Holding · next +9d (27 Jun 2026)
Production agentic-AI costs at scale routinely run multiples of POC projections, and a layered optimisation programme c…
- AM-003 · Partial · next +9d (27 Jun 2026)
GPT-5 Pro's tiered-subscription model forces enterprises to classify problems by computational difficulty — $200/month…