USA, May 7, 2026
For the most part‚ AI systems look good in demonstrations․ Dashboards look clean․ The accuracy metrics appear good‚ and stakeholders are pleased․
However‚ most problems are found when the system is actually used in the presence of real users facing unforeseen inputs and constraints․
One of the most valuable ways to assess and improve compliance with the AI RMF is through AI red teaming‚ which helps organizations discover failure modes before their customers‚ regulators‚ or adversaries do․
The National Institute of Standards and Technology developed the AI Risk Management Framework (AI RMF) to provide organizations with a non-document-centric way to manage AI risk throughout the governance‚ mapping‚ measurement‚ and management (M4) phases of the AI lifecycle and to encourage showing the effectiveness of risk controls․
Red teaming provides that evidence․
At Logicalis‚ we make structured AI testing an operational capability‚ particularly where the systems are having high impact on decisions․
AI Risk Is Multidimensional
Whereas customary software fails in predictable ways‚ AI systems do not․
A system may appear to function correctly while hidden risks remain․
Examples include:
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Prompt manipulation can alter behavior․
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Adversarial inputs: inputs designed to fool the model․
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Performance drift through changes in data or usage patterns
Results for different groups were mixed․
Confident answers are provided when uncertain
Meeting the AI RMF's expectations means recognizing the multi-dimensional and evolving nature of AI risk․
NIST published additional guidance on adversarial machine learning‚ including a threat taxonomy of attack methods‚ opponent objectives‚ and mitigations to enable organizations to threat model their AI systems․
Red teaming directly connects that theory to day to day operations․
What is AI Red Teaming in Practice?
AI red teaming is a structured method․
The teams will probe a system the way a malicious actor‚ careless user‚ or stressed employee might try to find cracks before they can cause damage․
Testing is commonly used to find the following defects:
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Unsafe or misleading outputs
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Guardrails either fail‚ or policies are bypassed
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Exposure of sensitive information
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Decision inconsistency with unusual inputs
The Cybersecurity and Infrastructure Security Agency incorporates AI red teaming into the testing‚ evaluation‚ verification‚ and validation of an artificial intelligence (AI) system during the AI lifecycle․
This mirrors the situation with software assurance‚ where AI testing is taken seriously․
Once organizations adopt the AI Risk Management Framework‚ red teaming becomes a continuous activity rather than a one time exercise․
Three Red Team Testing Areas Most Organizations Need
Whereas many teams are only trying to test for known risks‚ red teaming is meant to assess unknown risks․
Testing in all three areas is usually helpful․
Misuse and Abuse Scenarios
Test cases should anticipate users trying to get the system to behave in unintended ways․
In generative AI models‚ it also applies when an user manipulates prompts‚ given conflicting instructions‚ or tries to generate prohibited content․
For decision support systems‚ testing may consist of attempting to change the outcome by manipulating inputs․
Adversarial Machine Learning Attacks
Adversarial testing seeks to generate inputs that trick or subvert the model under test․
NIST's guidance for adversarial machine learning helps teams classify attacks and map them to mitigation techniques․
Operational Stress Testing
It is also important to test the AI under true operating conditions․
Challenges include the scale of use‚ time pressure‚ the quality of data involved‚ and the poorly-covered edge cases in the training datasets․
Model drift‚ silent failure‚ and variability are common problems with models during operational testing․
Fully implementing the AI RMF will require organizations to document tests of all three kinds․
Linking Red Teaming to the AI Risk Management Framework
For this reason‚ we expect that most organizations will find it relatively straightforward to map red teaming to the AI Risk Management Framework․
Govern
Organizations also specify who is responsible for red team testing‚ what can be tested‚ and who can stop deployments from going live․
Map
Specify the AI systems being tested‚ the impacted stakeholders‚ and the data flow through the system․
Measure
These tests evaluate not only accuracy‚ but also robustness‚ the frequency a policy is bypassed‚ and the failure rate and stability․
Manage
Organizations define how to remediate findings‚ how to retest for remediation‚ and how to set acceptable risk․
When properly implemented‚ this framework seeks to turn AI RMF compliance from a paper exercise into practice․
Measuring What Matters
Red team results must produce signals and not mere descriptive reports․
Metrics allow a leadership team to monitor progress and risk over time․
Common examples include:
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Number of critical failures found for each testing cycle
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Percentage of vulnerabilities remediated within a defined timeframe
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Rate of recurring failures after remediation
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Plans to address bypassing guardrails on high-risk prompts
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How often sensitive data exposure is attempted and successful
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Indicators of model drift tied to real business outcomes
NIST guidance on security and privacy controls stresses the usefulness of monitoring and assessment methods that produce evidence rather than mere increased confidence․
Retesting Is Where Real Governance Happens
One of the most common mistakes organizations make is treating red teaming as a compliance activity․
For AI RMF compliance‚ retesting is needed after remediation‚ updates to the system‚ changes to the AI vendor‚ new datasets‚ or new user workflows․
Because AI systems are constantly changing‚ testing programs also must․
A number of organizations have switched from annual large scale testing exercises to smaller more frequent 'sprints' which can provide faster feedback and better governance․
Building a Sustainable Testing Program
One-off red team tests are not hard to set up‚ but developing a consistent red team test program takes discipline․
Organizations should have a well-defined scope‚ understand opponent goals‚ have measurable failure criteria‚ and have a remediation process․
At Logicalis we help organizations build AI red teaming programs that are realistic‚ repeatable and aligned with business risk․ Use cases and attack scenarios are prioritized based on business value‚ and findings are translated into mitigations to reduce exposure․
The goal of AI RMF compliance is not the production of well-created reports‚ but increasing and maintaining control of the AI system․
Trust Comes From Testing
One of the simplest ways to understand if an AI system is strong or brittle is to push it to extremes․
For organizations that improve their AI risk management processes‚ the goal is to attempt to break their own systems; in other words‚ to identify vulnerabilities before others do․
Red teaming is another professionalized form of AI governance․
If it works‚ it turns this AI risk into something that can be measured‚ monitored and managed‚ instead of being an unknown unknown․
References
- National Institute of Standards and Technology
AI Risk Management Framework - NIST AI 100 2e2025 Adversarial Machine Learning taxonomy and mitigations
- Cybersecurity and Infrastructure Security Agency
AI Red Teaming and Software Test Evaluation Verification and Validation guidance - NIST SP 800 53 Revision 5 Security and Privacy Controls