APromptRiskDBThreat intelligence atlas
AI Mitigation

User Training - AI Mitigation

Educate AI model developers to on AI supply chain risks and potentially malicious AI artifacts. Educate users on how to identify deepfakes and phishing attempts.

AI MitigationBusiness and Data UnderstandingData PreparationML Model EngineeringML Model EvaluationDeploymentMonitoring and MaintenancePolicy

Record summary

A quick snapshot of what this page covers.

Techniques6Attacks this defense is designed to help with.
Lifecycle6Where this defense applies in the AI lifecycle.
Categories1How the source groups this defense.

Control summary

What this defense is meant to help prevent.

ATLAS ID
AML.M0018
ATT&CK external ID
M1017
Priority score
30
Business and Data UnderstandingData PreparationML Model EngineeringML Model EvaluationDeploymentMonitoring and Maintenance
Policy

Covered techniques

Attacks this defense is designed to help with.

AML.T0052.001 - Deepfake-Assisted Phishing

feasible

Train users on deepfake threats, including how to recognize synthetic voice, video, and text. Recommend verification through independent channels (e.g. known call-back number) before processing sensitive requests or providing sensitive information via voice or video calls.

AML.T0052 - Phishing

realized

Train users to identify phishing attempts by an adversary to reduce the risk of successful spearphishing, social engineering, and other techniques that involve user interaction.

AML.T0011.000 - Unsafe AI Artifacts

realized

Train users to identify attempts of manipulation to prevent them from running unsafe code which when executed could develop unsafe artifacts. These artifacts may have a detrimental effect on the system.

AML.T0011 - User Execution

realized

Training users to be able to identify attempts at manipulation will make them less susceptible to performing techniques that cause the execution of malicious code.

Source

Where this page information comes from.