the ai safety problem

We spend endless hours marveling at what generative AI can do, painting pictures, writing code, composing music. Rarely do we pause to ask what it should do, and what could be prevented. The AI stacks of 2025 deliver massive computational power with almost no friction, but they have almost no built-in mechanisms for verification, enforcement, or audit. Without these, outputs circulate unchecked, exposing users to harm and developers to liability, and the speed at which models generate content already outpaces both regulatory oversight and human moderation.

why verification matters right now

Minors can already interact with models capable of producing sexually explicit or otherwise unsafe content. At the same time, privacy-focused tools often forward every prompt to third-party APIs, creating single points of failure that can leak sensitive conversations or creative work. Generative AI workflows frequently expose proprietary ideas, personal queries, or private data to models without users’ knowledge, and these prompts can be incorporated into model training without consent. Regulators are responding. The EU AI Act and GDPR’s privacy-by-design framework require auditable safeguards and verifiable provenance. Several U.S. states are passing disclosure laws that push platforms toward demonstrable safety guarantees. Platforms without technical means to prove compliance risk regulatory penalties, exclusion from sensitive markets such as healthcare and education, and erosion of user trust. Cryptographically verifiable controls offer a pathway to safely scale AI deployment while maintaining user privacy.

three proposed pillars of a verifiable control plane

These are ideas for what a verifiable control plane could look like. The first pillar is verifiable model integrity. Zero-knowledge proofs (zk-proof) could cryptographically show that an output originates from an approved, safety-aligned model without revealing internal parameters or training data. Implementations might use zk-SNARK or zk-STARK constructions optimized for neural network inference, producing proofs that verify correctness without exposing model weights or training sets. Using zero-knowledge machine learning (zkML), models can confirm the correctness of a response and produce verifiable proofs without accessing raw prompts, preserving user privacy and reducing the risk of data leakage. The second pillar is private identity attestation. Decentralized identity wallets could issue cryptographic credentials asserting that a user meets policy requirements such as age or jurisdiction, which can be verified without revealing any personal identifiers. The third pillar is on-device pre-screening. Lightweight classifiers running locally could evaluate prompts or outputs against policy before they leave the device, detecting unsafe content while remaining efficient on consumer hardware. These layers could be combined in various ways depending on platform risk profiles, creating composable, auditable controls that may also reduce operational costs.

what a safe-by-design workflow could look like

In a practical scenario, a generative AI app might require an age-attestation credential in order to access mature filters from a decentralized identity wallet, receiving a zero-knowledge proof confirming the user is over eighteen without exposing their birthdate. When the user enters a prompt, an on-device classifier could evaluate it for unsafe language, sexual content, violence, or extremist content. Only policy-compliant prompts would be sent to a cloud endpoint, where a model running in a zero-knowledge-compatible environment generates the output along with a cryptographic proof confirming it came from the approved model. The client verifies the proof before displaying the output. Immutable logs of proof hashes and anonymized attestation status could be written to a tamper-evident ledger or secure storage, allowing auditors to verify compliance without accessing raw user data. This approach could scale across text, images, video, and multi-modal AI, creating a verifiable trail of safe operations while preserving privacy. zkML also opens the door to fair value for user contributions. Instead of models passively consuming prompts as free training data, users could retain control over their inputs, receive proofs of how their prompts are used, and even be compensated for contributing to model improvement, closing the loop and encouraging engagement.

scalable alternatives and trade-offs

Fully decentralized approaches reduce trust assumptions but introduce latency, higher computational cost, and deployment complexity. Centralized systems are faster and simpler but concentrate liability and reduce privacy guarantees. Hybrid architectures could combine on-device screening with centralized verification, balancing speed, privacy, and auditability. Standardized protocols for zk-proof verification, decentralized identity, and on-device classifiers would allow interoperability and reduce friction for adoption, enabling developers to incrementally integrate layers according to operational constraints and risk tolerance. Incorporating zkML for privacy and verifiable model outputs also reduces the risk that sensitive prompts are exposed or exploited by model providers while maintaining verifiable assurances of correctness.

enforcement mechanisms and real-world precedents

Apple’s on-device NSFW detection and Google’s SafetyNet attestations demonstrate that local enforcement and cryptographic verification are feasible at scale. GDPR and the EU AI Act already mandate traceability and logging for high-risk AI systems, requirements that zk-proofs and auditable logs could satisfy. Implementing these mechanisms shifts liability away from downstream integrators while providing regulators with verifiable evidence of safe operations without exposing sensitive user data. Integrating privacy-preserving proofs and verifiable ML could establish a precedent for compensating users for their contributions to model training, aligning safety, privacy, and economic incentives.

who builds it and who benefits

Open-source communities could provide reusable primitives for zkML, identity protocols, and on-device classifiers. Platform providers could operate verifiable compute endpoints and publish model integrity hashes. Identity-wallet projects could issue reusable attestation credentials to expand adoption. Developers would integrate these components to accelerate time-to-market, reduce compliance burdens, and enforce privacy-by-design. Regulators could verify compliance without accessing personal data, and users would benefit from safer, privacy-preserving services while retaining agency over their prompts and receiving compensation when appropriate. This ecosystem aligns incentives across builders, regulators, and users, creating infrastructure for responsible AI at scale.

how this improves the landscape

Filtering unsafe content before it leaves the device reduces the propagation of illegal or harmful material. Privacy-preserving attestations enable enforcement of policies at scale without storing personal identifiers. Cryptographic proofs make audits measurable and reduce operational overhead. Verified outputs could serve as market signals for responsible practices, nudging the broader AI ecosystem toward safety and privacy standards. Users gain control over their data and the potential to be compensated for contributing to model improvements, shifting the economics of AI toward fairness. Over time, these mechanisms could establish a verifiable baseline for AI services where safety, privacy, and fair contribution are inherent, measurable properties.

closing thoughts

The AI ecosystem in 2025 lacks a structured control plane capable of verifying model outputs, enforcing policy, and preserving privacy at scale. The ideas outlined here, namely, verifiable model integrity, private identity attestation, on-device pre-screening, and privacy-preserving zkML, represent one possible path forward. The building blocks exist, but widespread adoption requires coordination between open-source communities, platform providers, identity projects, and regulatory stakeholders. Implementing such a control plane could allow AI services to operate at scale with measurable safety, privacy, compliance, and fair treatment of user contributions, providing a foundation for trust in a rapidly evolving landscape.

We already know how to make AI faster; the question now is how to make it accountable.

Implementing a verifiable control plane has clear challenges. zk-proofs for large neural networks are still computationally expensive, which could bottleneck high-throughput applications. On-device classifiers may misclassify content, generating false positives or negatives, particularly with ambiguous or culturally specific inputs. Decentralized identity systems depend on robust adoption, credential issuance security, and resistance to cloning or replay attacks. Hybrid architectures introduce orchestration complexity between local and centralized systems. Potential remediations include optimizing zk-proof circuits for inference, continual retraining of classifiers with active learning, leveraging hardware-backed secure enclaves for credential storage, and using modular protocols that allow incremental integration and testing. Feedback loops from auditors and regulators can refine thresholds and policy enforcement over time.

The cryptographic primitives that underpin zk-proofs and private attestation rely on assumptions about computational hardness. Advances in quantum computing pose a risk to these assumptions, particularly for proof systems based on discrete logarithms or factoring. Post-quantum-resistant constructions such as lattice-based SNARKs and hash-based signature schemes provide a potential path to future-proof proofs and attestations, but they come with larger proof sizes and higher computational cost. In a hybrid system, quantum-resistant algorithms should be considered for both model integrity proofs and identity credentials. Quantum threats also introduce operational risk: if a sufficiently powerful quantum computer becomes available, previously recorded proofs could be retrospectively compromised, highlighting the need for continuous key rotation, periodic algorithm upgrades, and monitoring for emerging quantum capabilities. Building a verifiable control plane today requires factoring in quantum resilience as part of the design, even if practical quantum attacks remain speculative, because AI outputs can remain relevant for years and need long-term trust guarantees.