Red-Teaming AI
Adversarial testing of AI systems.
"Pay someone to break it before users do."
What is Red-Teaming AI? Adversarial testing of AI systems. Pre-deployment risk reduction.
Standard practice for major model launches.
Pre-deployment risk reduction.
Internal and external red-teaming. Continuous, not one-off.
Pick what to reward the model for.
Adversarial testing of AI systems. In the wild: Standard practice for major model launches.
Pick a lever. There are no neutral ones — every incentive funds a behavior somewhere.
Pick a reaction to Red-Teaming AI
One tap. We'll point you at the most useful next surface based on how this hits.
The full taxonomy entry
Every concept in the Atlas uses the same structure — so Red-Teaming AI can be compared, recombined, and cited like an element on a periodic table.
- Business
- Leadership
- Government
- Healthcare
- Education
- Sales
- Marketing
- AI
- Negotiation
- Media
- Public Policy
- Relationships
- Where in our org would Red-Teaming AI most often show up unnoticed?
- Which metric, ritual, or contract clause quietly rewards Red-Teaming AI?
- If we removed every payoff for Red-Teaming AI, what behavior would replace it?
- Who benefits when Red-Teaming AI persists — and who pays the cost?
- People defend the status quo using the language of red-teaming ai.
- Decisions cluster around the easiest narrative rather than the strongest evidence.
- New data changes the slide deck but not the decision.
- Anyone naming the pattern is treated as the problem.
Every Atlas entry is a node in a knowledge graph. See the related rail below to follow the connections.
See Red-Teaming AI through 4 lenses
Each layer of the Incentives OS reframes this concept with its own thinkers, vocabulary, and diagnostic question.
- Layer 8Organizational Psychology
What is the org actually rewarding — versus claiming to reward?
- Layer 11Economics & Mechanism Design
Who pays, who is paid, and what does the price signal hide?
- Layer 15AI & Alignment
What proxy reward is the AI optimizing — and what is it ignoring?
- Layer 21Mental Models & Mastery
Which model — or stack of models — are we missing here?
Do you actually know Red-Teaming AI?
Three quick questions. Result is saved into your review streak — come back when the term is due to lock it in.
Which best describes Red-Teaming AI?
Worked example, counter-example & concept map
On-demand AI analysis grounded in the Lab's research. Cached on your device after first run.
When you encounter Red-Teaming AI, your amygdala tags it as threat before your reasoning brain even knows what happened — and threat wins the first move.
Threat detection, fear, social pain, loss aversion, fast emotional tagging. Loss feels roughly twice as bad as equivalent gain feels good. Social rejection lights up the same circuits as physical pain.
See Amygdala in the Brain Atlas →Picked for you, from the Atlas
Ranked by shared learning paths, overlapping chips, and what you've saved.
When the agent acts, who's responsible?
Categorizing AI use cases by risk level.
Systematic skew in model behavior across groups.
Testing model behavior on hypothetical alternate inputs.
Adding noise to data to protect individual privacy.
Quantitative measures of model behavior across groups.
Send the card, not just the link
A pre-rendered social card with the title, eyebrow, and URL. Copy the link, post it anywhere, or download the SVG for slides.
More definitions to follow
Every term in the Atlas connects to a dozen others. Pick any of these and see where it takes you.
Perceiving meaningful connections in unrelated things.
The energy needed to refute bullshit is an order of magnitude bigger than to produce it.
Producing fabricated memories or explanations without intent to deceive.
Innovators → early adopters → early majority → late majority → laggards.
Outsourcing self-worth to audiences corrodes intrinsic direction.
Professors rewarded by student evaluations have an incentive to inflate.
Opaque billing rules create lucrative work for administrators and revenue-cycle firms instead of care.
Train a smaller model to imitate a larger one.
Departments set ticket/arrest quotas as performance metrics, rewarding officers for volume rather than safety.
Punishing failure more than rewarding success kills the conditions for innovation.
Dense legal codes advantage well-resourced insiders who can navigate them.
Concentration risk on a single AI provider.