There is a question that comes up in almost every conversation I have with founders and product leaders at generative AI companies. It does not always get asked directly — sometimes it surfaces as a concern about a specific feature, or a worry about what happens if a particular use case gets picked up by the press. But underneath all of it, the question is usually the same:
Could our product be used in a way we never intended — and would we even know?
The honest answer, for most GenAI companies at the growth stage, is: probably yes, and possibly not.
That is not a criticism. It reflects something real about how generative AI development works. Products are built fast, capabilities compound quickly, and the safety frameworks that were appropriate at launch rarely scale at the same pace as the product itself. The gap that opens up between what your platform can do and what your trust and safety infrastructure was designed to handle — that gap is where platform risk lives.
The risks are specific. Image, video, and audio tools can create or alter child sexual abuse material (CSAM), including deepfakes that sexualize real children. Text and voice systems can groom, impersonate trusted people, script abuse, generate exploitation instructions, and help distribute CSAM at scale. Some actors already sell custom CSAM generators online. These are not hypothetical edge cases. They are documented threat vectors that platforms at various stages of growth have already encountered.
This piece is about mapping that risk honestly — and sharing what the companies thinking most clearly about generative AI platform integrity are already doing about it.
What makes generative AI different from previous platform risk challenge
Platform misuse is not a new problem. Tech platforms have grappled with it for two decades. But generative AI introduces characteristics that make the risk calculus genuinely different — and that pre-existing trust and safety frameworks may not have been designed to address.
The first is creative agency. Previous platforms distributed content. Generative AI platforms create it. That is a meaningful shift in responsibility. When a bad actor uses your platform to produce something harmful — a coercive script, a synthetic image, a cloned voice — the platform is not just the channel. It is part of the production process.
The second is multimodality. A GenAI product that starts as a text tool frequently evolves to include voice, image, and video capabilities. Each modality expansion is simultaneously a product decision and a platform integrity decision. The surface area for AI misuse does not grow linearly — it compounds with each new capability. A voice cloning feature added to a text generation product, for example, immediately enables a new class of social engineering and impersonation risk that neither capability created alone.
The third is personalization and memory. AI companions and assistants that remember user preferences, adapt to communication styles, and build emotional rapport over time are genuinely valuable products. They are also systems where the relationship between platform and user becomes more intimate — and more exploitable — than anything that came before. A companion AI with persistent memory is a categorically different risk surface from a stateless chatbot.
None of these characteristics are reasons not to build. They are reasons to build with a clearer map of what you are taking on.
The AI misuse scenarios most GenAI teams have not fully mapped
GenAI teams already have a long list of risks to manage: copyright infringement, malware generation, fraud, biochemical weapon guidance, illegal content, and more.
Many teams have begun building policies, classifiers, red-teaming processes, and escalation paths for these harms. They should place child safety, and particularly online child sexual exploitation and abuse (OCSEA), within the same risk map.
That means looking beyond obvious prohibited outputs and testing for the indirect and compound misuse scenarios that can emerge as products mature, user bases grow, and capabilities expand.
Scripting and social engineering at scale
Text generation makes it possible to produce highly personalized, convincing scripts for coercion, manipulation, and deception — far faster and more effectively than any individual could manage alone.
A bad actor using a text generation tool to craft targeted sextortion messages, for example, can operate at a scale and level of personalization that was previously impossible. The platform may never generate anything that triggers a content filter, because the harmful output is not explicit content — it is persuasive language deployed in a harmful context off-platform.
Cross-platform exploitation chains
Your platform may be one step in a workflow you cannot see. A generative text tool used to craft a manipulation script, combined with a voice cloning tool to impersonate a trusted contact, combined with an image generator to create false evidence — used in sequence, these enable forms of harm that no single platform could have anticipated individually.
Understanding your product’s potential role in a wider AI misuse ecosystem matters, even if you never see the other steps.
Companion AI and the normalization risk
AI companions designed to build rapport, remember context, and respond empathetically represent one of the most significant emerging risk surfaces in consumer GenAI.
These systems can be manipulated into validating harmful behavior patterns, normalizing escalating content requests, or facilitating the kind of sustained, emotionally intimate interaction that grooms rather than supports.
The risk is not theoretical — it is an active area of exploitation that platforms in this category are already managing.
Open and customizable model risk
Highly customizable or fine-tunable models create a downstream risk that is genuinely difficult to manage.
Bad actors who fine-tune models and redistribute them as dedicated abuse generators — sometimes sold openly on secondary marketplaces — operate entirely outside the visibility or enforcement reach of the original developer.
This is a documented pattern, not a hypothetical one.
“Our users are not children.” That is not the same as not having a child safety risk.
This is the blind spot that comes up most consistently in conversations with AI founders — and it is the one that tends to generate the most significant legal and reputational exposure.
The assumption is logical: if children cannot access the platform, the platform does not carry child safety risk. But adult-only access policies do not eliminate child safety exposure. Children may still reach the product through shared devices, borrowed accounts, or age-gating approaches that rely on imperfect checks. And even when every registered user is an adult, those users can still use generative AI tools in ways that create risks for children.
There are two distinct pathways through which adult-only generative AI platforms create meaningful child safety exposure, regardless of who the registered users are.
The first is content generation and exchange. Adult users on adult-authorized platforms can generate, share, and in some cases monetize synthetic content that constitutes online child sexual exploitation and abuse (OCSEA). Age-gating prevents minors from accessing the platform — it does not prevent adult users from generating AI-generated OCSEA. This is an active enforcement challenge for platforms that did not design their systems with this threat in mind.
The second is off-platform harm enablement. Generative AI tools that can produce grooming scripts, coercive messaging, deceptive personas, or manipulative roleplay scenarios do not need a minor on the platform to cause harm to children. A text generation tool used to craft grooming communications, or an image tool used to create synthetic material for distribution, causes harm that originates on your platform and is executed elsewhere. The platform’s role in that chain carries responsibility even when the direct victim never interacts with your product.
A useful diagnostic checklist for your team:
Questions your team should already be able to answer
The following questions are not a compliance checklist. They are a diagnostic — a way of identifying where your trust and safety infrastructure is solid and where gaps exist that are worth addressing before they surface in a more difficult context.
On product development:
- When a new capability is added to your product — a new modality, a memory feature (the ability to retain context and personal information across user sessions), a persona mode — is potential AI misuse assessed before it ships, or after the first incident?
- Do you have a documented safety-by-design process that runs in parallel with product development, or is safety review a post-launch function?
On testing and red-teaming:
- Have you established legal and operational protocols for child-safety red teaming before testing begins? Legal counsel should be involved early in defining methodologies, escalation procedures, and reporting obligations. Red-team exercises should be designed to evaluate safety failures and exploitation risks without intentionally prompting for, generating, seeking, or handling CSAM.
- Are you testing the effectiveness of your controls against both known and previously unknown CSAM, including content-upload scanning, output moderation, and classifier performance?
- Are you running adversarial testing specifically for child safety misuse vectors, including grooming, coercion, manipulation, dependency-building, sexual solicitation, age-deception, and other exploitation-related behaviors?
- Does your testing evaluate whether attackers can bypass safeguards through multi-turn conversations, role-play, prompt injection, persona manipulation, or other techniques designed to evade classifiers and moderation systems?
- If your red-team testing, model evaluation, or dataset filtering surfaces suspected CSAM or other extreme material, do you have documented procedures for handling, escalation, review, removal, and where required reporting? Are the findings from child-safety red-teaming tracked, remediated, and re-tested before deployment decisions are made?
On detection and classifiers:
- Are your safety systems designed to detect both known CSAM and suspected (previously unknown) CSAM in model inputs, outputs, user-uploaded media, and training or fine-tuning datasets?
- Are your content classifiers trained to detect behavioral patterns — coercion, escalation, manipulation — or do they primarily catch explicit keywords and known harmful content?
- Are your classifiers scanning prompts before the model responds, the outputs that the model delivers, or both?
- Does your content moderation go beyond classifiers — covering user-uploaded and AI-generated media scanning, prompt blocking, and human review of automated decisions?
- Could a sophisticated bad actor route around your current safeguards by staying within the boundaries of your classifier training data?
On reporting and escalation:
- When harmful content is detected, do you have a documented process for escalation and reporting to relevant authorities?
- Are your team members clear on when reporting is legally required?
- Do you have relationships with the relevant reporting bodies — NCMEC, INHOPE, IWF, or regional equivalents — before you need them?
On ecosystem awareness:
- Do you have any visibility into how your platform is being discussed in communities where AI misuse is coordinated?
- Are you sharing or receiving threat intelligence signals from other platforms about bad actors operating across multiple services?
On governance and transparency:
- Do you have published child safety standards?
- Could you produce a transparency or safety report if a regulator, investor, or journalist requested one today?
If several of these questions don’t yet have clear answers, that’s worth paying attention to — it’s likely where some of your most significant platform risks lie. It’s also a more common situation than most leadership teams would publicly acknowledge.
You’re not alone in working through this. These are live conversations happening regularly in Tech Coalition member working groups — between product leaders, trust and safety practitioners, and legal teams building the same kinds of systems and facing the same kinds of questions.
The value of those conversations is precisely that they happen between peers, without the pressure of public scrutiny.
What operational trust and safety maturity looks like
The GenAI companies that are ahead on platform integrity are not necessarily the largest or best-resourced. What they share is a particular orientation — they treat safety as a product discipline rather than a compliance function.
In practice, that means several things.
Safety by design rather than safety by exception. New features are assessed for AI misuse potential as part of the product development process, not after a user report or a press inquiry forces the question. This is not about slowing development — it is about making better decisions with information that is already available. A voice cloning feature, for example, assessed for impersonation and social engineering risk before launch is a very different product decision from one assessed only after the first documented misuse.
Classifiers that detect behavior, not just content. Moving beyond keyword blocklists toward systems that can identify patterns of coercion, escalation, grooming, and manipulation — the kinds of harmful interactions that never trigger a content filter because no individual message crosses a line in isolation.
Cross-platform threat intelligence. Bad actors migrate across services, often systematically. Platforms sharing and receiving signals about known threat actors and AI misuse patterns are operating with significantly better visibility than those treating their platform as an isolated environment.
Red-teaming with a documented response protocol. Adversarial testing is necessary — but the process itself carries legal considerations. Involve legal counsel in scoping what can be tested and how, and have a clear escalation and reporting protocol in place before testing begins, not after.
Governance infrastructure that can be demonstrated. Investors, enterprise clients, regulators, and app store operators are all increasingly asking to see evidence of trust and safety maturity — not just assurances. Companies that can point to published standards, documented processes, and transparency reporting are in a measurably stronger position when those questions arrive.
The business case for getting ahead
Platform integrity failures in GenAI carry direct, measurable commercial consequences. This is no longer a reputational risk that exists in the abstract.
App store operators have demonstrated willingness to de-platform products without warning when synthetic media incidents surface. A single AI misuse incident involving non-consensual imagery or AI-facilitated exploitation can trigger removal from distribution channels that took years to build.
Venture capital due diligence at Series B and C now routinely includes trust and safety maturity assessments. A platform integrity crisis during a funding round does not just create negative press — it creates valuation risk and can derail or delay a round entirely.
Enterprise clients and advertisers treat platform integrity as a vendor qualification criterion. The question is no longer whether a business partner has terms of service — it is whether they have the operational infrastructure to enforce them.
Regulatory exposure is accelerating. Frameworks are developing across Europe, North America and Asia-Pacific. For companies with global ambitions, this is already a compliance reality.
The companies managing this well are not doing so because they have more resources. They started earlier, and they built their approach collaboratively rather than in isolation.
You don’t have to figure this out alone
The Tech Coalition is an industry alliance of more than 60 tech companies — including some of the largest platforms in the world — united around a shared commitment to preventing online child sexual exploitation and abuse.
For GenAI companies specifically, the TC offers something that is genuinely difficult to build independently: operational knowledge developed at scale, by peers who have already encountered these problems in production environments.
For those not ready for full membership yet, the Pathways program provides free access to select trust and safety frameworks, reporting considerations, and operational guidance — including resources built specifically for generative AI model companies addressing AI-generated OCSEA. It is designed for companies at exactly the stage where these questions are becoming urgent.
The Lantern signal-sharing program enables participating companies to share threat intelligence across platforms — so that a bad actor identified on one platform can be detected before they cause harm on another. Since launch, Lantern has facilitated more than two million signals and supported more than 350,000 enforcement actions across participating platforms.
Membership in the Tech Coalition means joining a peer network where these conversations happen regularly — not as policy debates, but as operational problem-solving between people who are building and running these systems at scale.
We’re here to talk.
If anything in this piece has surfaced a question your team does not yet have a clear answer to — that is worth taking seriously. Not because it means your platform is uniquely exposed, but because these are exactly the questions that distinguish companies that are ahead of this from those that are caught off guard by it.
The Tech Coalition is not here to audit your platform or tell you what to build. But if a conversation about where your product sits relative to these risks would be useful, I would be glad to have it.
You are welcome to reach out directly with the link below or explore the Pathways program.
FAQ
Can a generative AI product create child safety risks even if children are not users?
Yes. Generative AI platforms carry child safety exposure through two distinct pathways.
First, adult users can generate, share, or monetize synthetic content constituting AI-generated OCSEA regardless of age-gating on the platform itself.
Second, AI tools capable of producing grooming scripts, coercive messaging, or deceptive personas can facilitate harm to children that originates on the platform and is executed elsewhere. Age verification controls access — they do not control what adult users generate or how they use it off-platform.
What is AI-generated OCSEA?
Online child sexual exploitation and abuse (OCSEA) refers to the sexual exploitation and abuse of children facilitated through digital platforms and tools.
AI-generated OCSEA refers specifically to content — images, video, audio, or text — produced using generative AI tools that constitutes child sexual abuse material (CSAM) or that facilitates the grooming, exploitation, or abuse of minors.
This includes synthetic imagery generated without any real child victim, as well as text-based tools used to produce grooming or coercive content at scale.
How can generative AI tools facilitate grooming?
Generative AI text tools can produce highly personalized, psychologically sophisticated grooming communications at a scale and speed no individual could achieve manually.
AI companions with memory and emotional rapport capabilities can be manipulated into validating escalating behavior patterns. Voice cloning tools can be used to impersonate trusted adults. Image generators can produce synthetic material used in exploitation workflows.
In each case the AI tool itself may not directly interact with a minor — it provides capability that a bad actor deploys elsewhere.
What trust and safety infrastructure should generative AI companies have in place?
At minimum, GenAI companies operating a launched model should have:
- Published child safety standards
- Documented dataset governance and cleaning procedures to identify, remove, and review known and suspected CSAM before training or fine-tuning
- A safety-by-design process that assesses new features for misuse potential before launch
- Adversarial red-teaming that includes child safety misuse vectors
- Technical controls for detecting and blocking known CSAM and identifying likely novel CSAM in model inputs and outputs
- Behavioral classifiers capable of detecting coercion, grooming, and manipulation patterns beyond explicit keyword filtering
- A documented protocol for handling extreme material discovered during testing
- Governance infrastructure sufficient to produce a transparency or safety report on request
The Tech Coalition’s Pathways program provides detailed operational guidance across each of these areas.
What are the specific platform risks of AI companion products and memory features?
AI companions that build emotional rapport, remember personal context, and adapt to user communication styles over time create a risk surface that is categorically different from stateless AI tools.
These systems can be manipulated into normalizing harmful behavior patterns, validating escalating content requests, or sustaining the kind of emotionally intimate interaction associated with grooming.
Persistent memory features amplify this risk by enabling bad actors to develop and refine manipulative dynamics over extended periods. Companies building companion AI products should assess these dynamics explicitly in their safety-by-design process.
What is the business risk of not addressing AI platform misuse proactively?
The commercial consequences of platform integrity failures in GenAI are direct and measurable.
They include:
- Removal from major app store distribution channels following a synthetic media or exploitation incident
- Disruption or devaluation of funding rounds where VC due diligence now routinely assesses trust and safety maturity
- Loss of enterprise clients and advertising relationships
- Regulatory action under frameworks including the EU Digital Services Act
- Sustained reputational damage in a market where brand trust is a primary differentiator
Companies that address these risks proactively consistently report stronger investor, partner, and regulatory relationships than those that respond reactively.
How should AI companies think about cross-platform threat actors?
Bad actors who exploit generative AI platforms rarely operate on a single service. They migrate systematically, exploiting gaps between platforms and using multiple tools in sequence to accomplish harmful goals.
A GenAI company operating without cross-platform threat intelligence is effectively blind to a significant portion of the threat landscape. Participating in signal-sharing programs — such as the Tech Coalition’s Lantern program — gives platforms visibility into known threat actors and misuse patterns identified across the broader ecosystem, enabling detection and enforcement before harm occurs on their own platform.
Should AI-generated harmful content be reported in the same way as real-world abuse material?
AI-generated content that constitutes child sexual abuse material — including synthetic imagery with no real child victim — is reportable and in many jurisdictions legally required to be reported, in the same way as real-world CSAM.
However, the reporting process for AI-generated content carries specific considerations that standard reporting workflows may not account for — including how to document the generative context, preserve relevant metadata, and handle content that was surfaced during internal testing rather than user reporting.
The Tech Coalition has developed reporting templates specifically designed for AI-generated OCSEA that address these distinctions.
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