Can you trust social media? Or in fact any media outlet anymore?
Images, documents and digital evidence are now shared across social platforms, messaging services, marketplaces and informal channels. These submissions may relate to disputes, accusations, harassment, impersonation, scams or personal claims where the consequences are serious but the context is unstructured. In these situations, the ability to verify whether content is real, edited or generated by artificial intelligence becomes critically important.
Can you believe what you see anymore?
The rise of accessible AI tools has increased the risk of personal harm through manipulated content.
Not all digital risks fall neatly into defined industry categories. Individuals and organisations increasingly encounter digital content that sits outside structured workflows such as insurance, healthcare or retail, yet still carries significant personal, financial or reputational risk.
Images and video can be altered to misrepresent events, create false allegations or support scams and coercion. Documents can be fabricated to intimidate, deceive or extract payment. For individuals, the impact may include financial loss, emotional distress or reputational damage. For organisations, unmanaged personal content risks can escalate into legal disputes, safeguarding concerns or brand harm.
The General Authenticity Protection Model is designed to address these scenarios. It provides broad analysis of digital content where sector specific models are not applicable or where the risk relates primarily to personal protection and trust. This includes content submitted by individuals, customers, employees or third parties that requires verification before it is relied upon or acted upon.
Use cases may include assessing images used in personal disputes, validating evidence provided in complaints or allegations, reviewing content linked to scams or impersonation attempts, or supporting internal investigations where digital evidence does not fit a predefined category. The General Model acts as a first line of assurance, helping users understand whether content shows indicators of manipulation or synthetic generation.
This capability is particularly relevant as digital interactions increasingly occur outside formal systems. Messaging apps, peer to peer platforms and online communities are now common venues for transactions, disputes and communication. Evidence shared in these environments often lacks the structured checks found in regulated workflows, increasing the risk of misuse.
Humanly’s General Model does not make judgments about intent or outcome. It focuses on the integrity of the digital content itself. By helping users assess authenticity, it supports safer decision making, whether the context is personal, organisational or exploratory.
Importantly, the General Model also plays a role in future readiness. As new fraud patterns, social risks and AI enabled misuse emerge, organisations may encounter threats that are not yet well defined. The General Model provides coverage for these unknowns, allowing users to apply authenticity assessment while more specialised approaches evolve.
By offering a broad, adaptable layer of protection, Humanly enables both individuals and organisations to engage more confidently with digital content in an environment where trust can no longer be assumed.
Key Features
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