authenticity
What It Means
Authenticity means proving that something or someone is genuinely who or what they claim to be. In AI systems, this involves verifying that data sources, models, users, or system components are legitimate and haven't been tampered with or impersonated.
Why Chief AI Officers Care
Without authenticity controls, AI systems become vulnerable to data poisoning, model theft, deepfakes, and adversarial attacks that can corrupt decision-making and damage business reputation. Regulatory frameworks increasingly require organizations to demonstrate the authenticity of their AI training data and model outputs, making this a compliance necessity.
Real-World Example
A financial AI model trained on market data must verify that all data feeds come from legitimate financial exchanges rather than malicious actors injecting fake trading information. Without authenticity checks, attackers could manipulate the model's recommendations by feeding it false market signals.
Common Confusion
Authenticity is often confused with authorization or data accuracy, but it specifically focuses on proving identity and legitimacy rather than permission levels or correctness of information.
Industry-Specific Applications
See how this term applies to healthcare, finance, manufacturing, government, tech, and insurance.
Healthcare: In healthcare AI, authenticity ensures that patient data, medical imaging, and clinical models are verified as genuine a...
Finance: In finance, authenticity is critical for verifying the legitimacy of transaction data, customer identities, and AI model...
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Includes:
- 6 industry-specific applications
- Relevant regulations by sector
- Real compliance scenarios
- Implementation guidance
Technical Definitions
NISTNational Institute of Standards and Technology
"property that an entity is what it claims to be"Source: ISO/IEC_TS_5723:2022(en)
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