7 Effective Safeguards for Regulating Generative AI"
Generative AI is transforming industries at breakneck speed, but without proper safeguards, the technology poses serious risks to creators, consumers, and society at large. This article outlines seven practical measures that policymakers and companies can implement to ensure responsible development and deployment of AI systems. Drawing on insights from legal scholars, technologists, and industry experts, these recommendations provide a roadmap for balancing innovation with accountability.
Guarantee Human Oversight With Full Traceability
AI regulation for generative AI should stop treating "AI" as a single category and instead govern the full lifecycle of agentic systems: how they ingest, transform, and act on data in real workflows, with safeguards tied to clinical and operational impact. The most effective safeguard for these systems is mandatory, auditable human-in-the-loop oversight for high-impact uses, anchored in rigorous data governance and traceability so every output can be challenged and reversed.
Regulators should start with purpose limitation and data minimization, especially where generative agents handle PHI at scale. Policies such as collecting only the minimum data required for a defined clinical or operational objective and defaulting to de-identification or pseudonymization can be translated directly into regulatory obligations. Encryption at rest and in transit, data residency controls, and documented, auditable data flows between EHRs, IoT devices, and AI platforms should be table-stakes for any generative deployment in healthcare.
AI agents are now workflow actors, not passive decision-support: they schedule, chart, triage, and conduct outreach, often automating 90-99% of tasks like appointment scheduling or consult preparation. Regulation should therefore classify these agentic systems as operational actors and apply controls similar to high-risk clinical software, including process-level risk assessment, strict separation of production and test environments, and formal go/no-go criteria before agents can influence patient care.
The key safeguard is governed human-in-the-loop oversight wherever generative AI can materially affect care, access, or trust. That should include mandatory human review for AI-initiated high-impact actions, traceability of which data and models influenced each output, and built-in mechanisms to override or correct AI recommendations, all backed by immutable audit logs.
Baseline expectations should include RBAC with least privilege, MFA for privileged access, continuous monitoring, and tested incident response for all systems processing sensitive data. Vendors must be held to equivalent standards through contracts, due diligence, and ongoing risk registers, while patients receive clear notice of AI use, meaningful consent and opt-out options, and timely responses to access and deletion requests—especially in sensitive domains like elderly care, where AI agents are woven into daily life.
Require Prominent Disclosures and Boundaries
AI regulation should directly address the risk of users treating generative systems as human, especially in sensitive contexts. In our deployment, some users began treating the AI like a real therapist, so we set clearer boundaries and added recurring reminders about the system's limits to protect emotional well-being. The most effective safeguard regulators can require is prominent, repeated disclosures that define capabilities, limitations, and appropriate use at key points in an interaction. This keeps expectations realistic and reduces the chance of harmful over-reliance. Clear boundary messaging should be simple, visible, and consistent throughout the experience.
Impose Direct Liability for Claims
The real challenge is we're regulating AI like it's software when it actually behaves more like hiring someone who might be unreliable.
Traditional software does exactly what it's programmed to do. You can test it and predict the results. Generative AI doesn't work that way. The same question gets different answers, and nobody, including the people who built it, can fully explain why it sometimes fails in bizarre ways.
The most effective safeguard would be treating AI outputs like advice from unlicensed professionals. If an AI makes a medical claim, legal interpretation, or financial recommendation, the company using it should be liable as if they made that claim directly. No hiding behind "the AI said it, not us."
Companies right now are letting AI do things they'd never allow an uncredentialed human to do because liability isn't clear. Make the liability obvious and watch how quickly companies get careful about where they actually deploy these tools.
Enforce License Transparency and Creator Compensation
For me, the most critical safeguard in generative AI regulation is the uncompromising protection of original human-created content. Copyright law has always recognised that creative works deserve protection from unauthorised use. That principle is not optional in the AI age, it is essential.
Generative AI systems are trained on vast datasets, much of which is already protected by copyright. When these systems generate outputs, they are building on the intellectual labour of authors, artists, photographers, writers, and creators who invested time, skill, and originality. Without safeguards, we risk a system where AI companies profit at scale while creators receive nothing.
The most effective solution is mandatory transparency around training data combined with proper licensing and compensation frameworks. Denmark provides a useful model here. AI companies should be required to disclose which copyrighted materials were used and to license that use, much as copyright law already requires for music sampling or adaptations. If an AI is trained on a photographer's work, that photographer deserves attribution and fair compensation.
AI-generated content must also be clearly labelled and distinguished from human-created work. Consumers and businesses have a right to know what they are engaging with. This transparency is vital for trust and for ensuring that human creators can compete fairly.
Copyright ownership must remain tied to human authorship. Content generated entirely by AI, without substantial human creative input, should not enjoy the same protection as human-created works. This preserves the value of human creativity and prevents AI companies from monopolising vast libraries of derivative content.
Because generative AI operates at unprecedented scale and speed, traditional enforcement tools are no longer sufficient. We need AI-specific mechanisms, including automated detection and streamlined dispute resolution. Denmark has already taken meaningful steps; other jurisdictions should follow. Innovation matters, not at the cost of dismantling the IP systems that creative industries rely on.
Effective AI regulation must place original human creativity at its centre. Transparency, compensation, and clear authorship rules are not barriers to innovation, they are what make sustainable innovation possible. Without them, AI risks destroying the very creative ecosystem it depends on.
Adopt Cryptographic Watermarks for Synthetic Media
Policy frameworks for GenAI solutions should consider deployment context as opposed to the model architecture. The GenAI output is Non-Deterministic as it depends on prompt and seed, and therefore will vary from instance to instance. The outcome is a strong emphasis on the need for a liability framework that separates applications of high-stakes risk, e.g., financial advice or guidance bot applications from less (lower) risk applications of GenAI (creativity), resulting in an environment that provides assurance for high-risk implementations and allows more creativity for lower-risk applications.
The best means of safeguarding content created by Generative Artificial Intelligence is through mandatory use of cryptographically-based watermarking at the model layer, as set forth in the C2PA Standard. Unlike visual disclaimers which can be cropped from images, cryptographically-embedded watermarks are considerably more difficult to counterfeit. As a result of this process, there will be a "chain of custody" from the creator to the user for synthetic media, allowing social media platforms and other downstream solutions to detect and label synthetic media prior to distribution to end-users.
Mandate Model Provenance and Verification Logs
AI regulation needs to recognize that generative models don't behave like traditional software—they create outputs probabilistically, adapt to new data sources, and can be influenced by prompts in ways even developers can't fully predict. The regulatory focus shouldn't be on restricting the models themselves, but on creating guardrails around how they're trained, deployed, and audited in real-world environments.
The most effective safeguard we could implement today is mandatory model-level provenance and verifiable audit trails. If regulators required companies to document training sources, fine-tuning datasets, safety layers, and post-training modifications—and make those records subject to independent review—we'd dramatically reduce the risks of hallucinations, hidden bias, covert data leakage, and misuse.
In the same way financial institutions maintain traceability for transactions, AI systems need traceability for decisions. Provenance doesn't slow innovation; it builds trust, accountability, and a foundation where the highest-value AI solutions can scale responsibly.
Define Accountable Owners Across the Lifecycle
As someone who has built scalable digital operations and integrated AI toolchains, this is my take on what the fundamental regulatory challenge is — and a safeguard that actually works in practice.
The most multilinear accountable and responsible AI training and usage is the most effective generative AI safeguard - because the biggest risk that advanced AI does in our implemented systems for outreach and link analysis that exchanges human efforts with AI in a feedback loop to distribute content is the unabated amplification of systemic biases, misattribution, or misinformation - is not primarily rooted in haphazard model building. These risks evolve when there's no clear owners downstream of the outputs in this human-AI workflow.
Regulations promptly addressing this by stipulating that there should be a defined actor in each critical juncture of the AI lifecycle - model creation, system/application depending on said model, deployment, User interface — with sufficient technology and operational mitigations to blunt this risk. The UK's approach of letting the regulator tailor this accountability instead of taxing the model maker as the single responsible party for everything to stem this risk is more realistic. Because in reality, we are observing teams leveraging open models and piping them through unique workflows where there would be more opportunities for unsafe prompt engineering or toxic training data to be weaponized instead of the model itself going "rogue".
Having this defined chain of responsibility enables us to audit harmful errors in the outputs to perform upstream interventions in the human-AI workflow to delink such harmful data from prospecting pipelines in order to avoid the amplification of these errors by our automated distribution systems. We even sit on 48% month on month reductions in AI Outreach Errors in one iteration of our deployment, thanks to the installation of this multilinear accountability system compared to other attempts to fix the model in isolation
What makes this a powerful effective safeguard is its inherent adaptability to allow for ecosystem diffusions in terms of actors — foundation model creators, application builders, operators. This the requirement for these accountable actors to be documented and auditable doesn't stifle innovation, rather it gives technologists, vendors, or even policy staffs a way of managing the space for innovation and ensuring compliance with regulation.
