Alert fatigue
The general security-operations phenomenon in which a reviewer facing high volumes of low-priority or routine alerts becomes desensitized and less likely to give any single alert, including a genuinely urgent one, adequate scrutiny. Illustrated in Section 4 by the Target Corporation breach; the underlying human-attention mechanism T10 names for agentic systems specifically.
Defined in 7 GAGE programs, which carry 10 distinct definitions of it. The wording above is taught in Certified Agentic AI Governance Professional (CAAGP).
How each discipline defines it
The same term does different work depending on who is using it. These are the definitions as each program teaches them, unedited.
The general security-operations phenomenon in which a reviewer facing high volumes of low-priority or routine alerts becomes desensitized and less likely to give any single alert, including a genuinely urgent one, adequate scrutiny. Illustrated in Section 4 by the Target Corporation breach; the underlying human-attention mechanism T10 names for agentic systems specifically.
The failure that occurs when a monitoring system fires so often, especially on normal variation, that the owner learns to ignore it, turning even the alerts that matter into noise. Prevented by setting thresholds where action is genuinely warranted and tiering alerts by severity.
The well-documented pattern in which people exposed to frequent false alarms begin to disregard alarms in general, including true ones. A warning system with a heavy false-alarm burden can produce alert fatigue and thereby cause the very missed cases it was meant to prevent.
The well-documented pattern in which clinicians or other users, faced with a high volume of low-value automated alerts, learn to override or ignore them by default, at which point the alert system stops improving decisions and starts generating noise instead.
The condition in which a team receives so many low-value or poorly targeted quality alerts that it begins to deprioritize alerts as a category, increasing the risk that a genuinely important alert is dismissed along with the noise.
Where it is taught
The exact lessons this term appears in. The first module of every program is free with a free account.
- Approvals in a Coding Agent · Meaningful Human Accountability, Certified Agentic AI Governance Professional (CAAGP)
- Oversight at Agent Speed · Meaningful Human Accountability, Certified Agentic AI Governance Professional (CAAGP)
- The OWASP Threat Map II · Technical Controls and Threat Modeling, Certified Agentic AI Governance Professional (CAAGP)
- Freshness, completeness, and the lie of the green dashboard · Quality as Physics, Certified AI Data Governance Professional (CADGP)
- Model drift: detecting the quiet degradation nobody reports · Evaluation and Trust, Certified AI Governance Professional (CAIGP)
- The evaluation report: evidence that your trust levels are earned, not hoped · Evaluation and Trust, Certified AI Governance Professional (CAIGP)
- Implementation Realities: Why Deployments Fail and What That Means for Policy · Technical Credibility Deep Dive, The AI Lobbyist: Certified AI Policy Strategist
- Process Governance and Continuous Improvement · Process and Operations Redesign, Certified AI Transformation Professional (CATP)
- Continuous controls monitoring and continuous assurance · Governance, Risk and Compliance Technology, Automation, and Evidence Engineering, Certified Enterprise Governance, Risk and Compliance Professional (CEGP)
- Bank alerts and the withdrawal threshold you choose · The Hardening Lab, Senior AI Scam Defense
Where it shows up at work
A definition is where Alert fatigue starts. The lessons above teach it inside the competencies below, which are what job descriptions ask for and what the GAGE credential records. Every link here is drawn from the same careers map that builds a role's readiness check.
Sets performance metrics, thresholds and review triggers after launch, and treats a model change, a vendor update or new data as a reason to re-check.
3 lessons on this page teach it as a primary outcome.
Defines who reviews AI outputs, what they check, when they can override, and how to keep review from becoming a rubber stamp.
One lesson on this page teach it as a primary outcome.
Designs decision rights, committees, intake, approval tiers and escalation so routine uses move and consequential uses get reviewed.
One lesson on this page teach it as a primary outcome.
Roles that hire for it
Each role page shows the full competency set, what the seat decides, and a readiness check against your own evidence.
- AI Product Manager, responsible product editionneeds it at depth · 3 open postings this week
- AI Auditorneeds working knowledge · 9 open postings this week
- Model Risk Managerneeds it at depth
- AI Engineer, production systems editionneeds working knowledge · 23 open postings this week
- AI Model Validatorneeds working knowledge
- VP of AI Governance and Ethicsneeds it at depth
How job descriptions say it
Postings rarely use the textbook word. These are the phrases the careers map reads as asking for the competencies above.
Where the frameworks place it: EU AI Act Article 72; NIST AI RMF MEASURE 3 and MANAGE 4; EU AI Act Article 14; ISO/IEC 42001 Annex A.
One interview question about it
A model has been in production for a year. What do you monitor, what threshold triggers a review, and who gets the alert?
Asked of candidates for post-deployment monitoring and drift detection. Answering it in your own words is exactly how a GAGE topic is passed, and the credential records the pass where an employer can check it.
If you come from another field
Some backgrounds already carry this competency, and the readiness check gives credit for it.
- Model risk, credit risk and quantitative analysis carries it as a claim to back up: Performance monitoring thresholds are familiar.
Terms it appears with
Not an alphabetical neighbourhood: these are the terms taught in the same lessons, ranked by how often they appear together.