Ai, web3 and metaverse – real-world applications

The combination of AI, Web3 and the metaverse makes sense when it addresses concrete needs: logging in across worlds, protecting data privacy and properly settling digital value. In practice, Web3 provides mechanisms for ownership, rule enforcement and the transfer of rights, while AI is responsible for automation, fraud detection and personalisation. In this article, we focus on proven implementation patterns that genuinely appear in metaverse projects, rather than repeating promises of “magical interoperability”. We will show how passwordless identity, verifiable credentials (VC) and ZK-based privacy work, as well as how to tokenise assets, licences and micropayments. Each section describes what to implement, for what purpose and which design decisions help maintain good UX. If you are planning a VR/AR world, an event, a marketplace or a loyalty programme, this material will help you organise the architecture around the user and business goals.

User identity and privacy protection in the metaverse

User identity and privacy protection in the metaverse are most often based on the SSI approach with DID and attribute verification without revealing the full set of data. Instead of creating separate accounts in every world, the user can log in with a signature via an identity wallet, using DID (e.g. W3C DID) and mechanisms such as Sign-In with Ethereum (SIWE). This allows the profile and access to be portable between metaverse applications without a central “login”. This approach also makes it easier to build consistent permissions (e.g. for staff areas) across multiple environments.

Verifiable credentials (VC) allow you to confirm age or permissions (e.g. “I am over 18”, “I am a certified welder”) without revealing sensitive information, supporting selective disclosure in solutions such as Polygon ID or SpruceID. This is particularly useful when 18+ zones or access restricted to employees operate in the metaverse, and at the same time you do not want to “collect everything” about the user. This is complemented by ZK proofs (e.g. zk-SNARK), which make it possible to prove that a condition is met (e.g. holding a token from collection X) without revealing the wallet address or the full account balance. In a metaverse covering sensitive activities (e.g. health or employee training), such a model helps reduce data exposure.

Identity protection in the metaverse also covers abuse and avatar impersonation, which is why cryptographic mechanisms are increasingly being combined with behavioural analysis. Cryptographic signatures can link an avatar to a key, and AI can assess behavioural biometrics (e.g. controller movement style) as a risk signal and trigger additional confirmation when suspicious changes occur. In Web3 projects, bot farms in airdrops, events and voting are also a common problem: AI identifies repetitive patterns (reaction time, VR movement trajectories, sequences of actions), while Web3 enforces the rules (e.g. blocking token claims, requiring additional credentials, rate-limit restrictions on an address). When high-value trading is involved, the “KYC once, use many times” pattern based on VC is also used, where AI automates document verification and flags transaction anomalies (e.g. rapid turnover of the same NFT).

1. 01Self-sovereign identity (SSI)DID and wallet
2. 02Secure loginSign-In with Ethereum (SIWE)
3. 03Portable profile and accessInteroperability without a central authority
4. 04Verifiable credentials (VC)Selective disclosure, data protection

The SSI, DID and VC approach enables portable, secure and privacy-protecting identity across multiple metaverse worlds.

Creator economy and asset tokenisation in virtual worlds

Creator economy and asset tokenisation in virtual worlds come down to the fact that rights, access and settlements are encoded and enforced by smart contracts, while AI supports both production and performance measurement. Rather than treating NFTs solely as “pictures”, projects use them as a licence carrier for 3D models, skins or music, so that the buyer immediately knows the terms of use. Conditions can be stored in metadata and enforced in the system itself (smart contract and validation in the game engine, e.g. Unity/Unreal). This makes it easier to distribute assets and organises the rules for commercial use in specific worlds.

NFTs as licences (with conditions in metadata and enforcement in the contract and engine) are a practical way to sell 3D assets while clearly controlling usage rights. Where users expect progression and “living” items, dynamic NFTs (dNFTs) are used, whose state can be updated by an oracle or an on-chain event record, while AI generates new variants (e.g. textures or animations). An example pattern is an evolving “pet” in the metaverse that changes based on user activity. When the priority is frequent and small transactions (e.g. entry to a VR concert or a short training module), layer 2 payments (e.g. Polygon, Arbitrum, Base) and a pay-per-use model in stablecoins (USDC) are used to reduce fees and unlock content without delay.

• Royalty enforcement: moving fees into the contract (e.g. fee mechanisms in the token or transfers only through permitted contracts) and AI monitoring of markets for circumvention attempts (e.g. OTC sales).
• Space tokenisation (LAND) and advertising: simpler leasing/subletting, and AI calculates KPIs (unique visits, time spent, heatmaps of avatar movement, on-chain/off-chain conversions).
• Wallet-based loyalty: NFT/POAP badges as a “loyalty card” between brands, with AI segmentation of behaviours and matching of rewards (e.g. discounts, early access) without a central account.
• Gig economy in the metaverse: roles such as moderators, guides, scene designers, VR trainers or event operators, with settlements via escrow and task matching plus quality control supported by AI (e.g. analysis of training recordings).

Tokenisation is also changing the way experiences are measured and monetised, because the metaverse makes it possible to combine digital ownership with behavioural analytics. In virtual spaces, brands can assess whether buying or leasing “plots” makes sense based on hard metrics (unique visits, time spent, avatar movement heatmaps and conversions), which AI can calculate and organise. At the same time, Web3 makes it easier to transfer benefits and entitlements between worlds (e.g. wallet-based loyalty), without needing to maintain central accounts. As a result, creators and brands can build revenue models based on licences, access and micropayments instead of relying solely on one-off sales.

AI in personalising the user experience

AI personalises experiences in the metaverse mainly by analysing user behaviour and dynamically adapting content to their needs. World owners use telemetry (movement paths, interactions, preferences) to improve retention and match content more accurately without intrusive tracking. Web3 can at the same time give the user greater control over data, for example by sharing only selected signals in exchange for tokens or discounts. This means personalisation does not have to mean handing over an entire behavioural profile to a single platform.

The most practical compromise is personalisation based on selectively stored “memory”: private off-chain (e.g. a vector database), and on-chain only when auditability is required (e.g. in games with an economy). In the metaverse, this translates into NPCs and conversational agents in VR that “remember” previous meetings and respond sensibly in later sessions. Dialogue control can be handled by an LLM (e.g. GPT-4/4.1, Claude, Llama), while memory storage can be limited to what genuinely supports the experience. When accountability matters, a hash or proof is written to the chain instead of the full conversation content.

Personalisation also covers communication and real-time user support, especially during events and VR shopping. ASR/TTS models (e.g. Whisper + TTS) enable “live” transcription and dubbing, while Web3 can settle micropayments for usage (e.g. payment per minute of translation) and manage consent for recording. In retail environments, AI acts as a concierge and shopping assistant (recommendations, comparisons, Q&A), while transaction completion and digital ownership can be handled by Web3 (e.g. NFT/receipts). In addition, AI can support safe zones by detecting toxic language and breaches of personal space, which improves the comfort of the experience.

1. 01Behaviour analysisTelemetry and interactions
2. 02Dynamic adaptationContent and retention
3. 03Web3 data controlSelective sharing
4. 04Hybrid memoryOff-chain and on-chain
5. 05Better experienceRelevant, non-intrusive

Balanced personalisation: AI increases engagement, while Web3 gives users control over data and privacy through selective sharing.

Interoperability and infrastructure in the metaverse ecosystem

Interoperability and infrastructure in the metaverse largely come down to decisions about where to store data, how to feed smart contracts with events from the 3D world, and how to standardise avatar and item formats. In practice, it is worth keeping critical information (e.g. hash, rights, identifier) on-chain, and heavier files (3D models, textures) in IPFS or Arweave. This split reduces problems when an asset “disappears” or stops loading, because metadata consistency can be verified independently. AI can automatically check hash consistency and detect content swaps.

Data flows between the metaverse and the blockchain are handled by oracles, which transfer tournament results, test outcomes or in-game engine events to the chain. For example, oracles (e.g. Chainlink) can pass on information about the winner, while AI reduces fraud through telemetry analysis (e.g. detecting aimbots, macros and inhuman reaction times). To help AI quickly build analytics, recommendations and scoring, teams use indexing and on-chain data querying, for example via The Graph, Dune, Flipside or their own indexers. This makes it possible to expose an API that can underpin, among other things, offer personalisation or the detection of suspicious activity in the economy.

If you want real interoperability, design it on two levels: asset standards (e.g. glTF/VRM) and ownership mapping (NFT) to specific model variants in Unity/Unreal. For avatars and skins, the key point is that different worlds have different requirements for formats and rigging, which is why AI can support automatic animation retargeting and model conversion to engine requirements. From an implementation perspective, wallet, NFT and payment integration usually relies on SDKs (e.g. Thirdweb, Moralis, WalletConnect) and a server layer for sessions and telemetry, while AI operates as a separate service (e.g. endpoints for NPC dialogue). In the context of sensitive data, a private data store (e.g. Ceramic/IDX or a custom vault) and tokenised consents (VC) are also used, and AI models are trained on aggregated or anonymised data rather than copying raw logs.

Security and compliance in AI, Web3 and metaverse projects

Security and compliance in AI, Web3 and metaverse projects require both robust enforcement mechanisms (smart contracts) and continuous fraud detection by AI. In practice, the risks do not end with a “contract hack”, because they also include phishing in VR, NFT market manipulation and AI model vulnerabilities (e.g. prompt injection). For this reason, teams combine auditable on-chain rules with behavioural and anomaly analysis off-chain. It is also important to plan dispute and incident handling in advance, because the metaverse is a “live” environment in which mistakes can escalate quickly.

AI can speed up and streamline smart contract audits (e.g. vulnerability pattern analysis, fuzz test generation), but the key decisions should still rest with the auditor. This is especially important where an error in the tokenomics ends in mass token theft or a “mint” exploit. In NFT markets, AI helps detect wash trading by analysing flow graphs, repeated sequences and address clusters, while Web3 provides the full history needed for verification. This makes it easier to filter out volume generated between linked wallets, which matters when valuing collections and virtual land.

• Phishing and account theft in VR: use wallets with transaction simulation (e.g. Rabby), trusted domain lists and AI warnings that recognise suspicious UI/content patterns and force confirmation of the signing target.
• Security for agents and NPCs: reduce the risk of prompt injection and data poisoning through tool sandboxing, output validation and content policies; cover critical actions (e.g. token transfers) with additional on-chain authorisation (multisig or a daily limit).
• Copyright and provenance: record the timestamp, file hash and licence in the metadata, and use AI to assess similarity and flag infringement risk (e.g. texture too similar to a brand).
• GDPR and biometrics in VR: collect only the necessary data, apply short retention periods and anonymise; for confirmations, use a hash/verifiable credential (VC) instead of raw recordings.

Security also covers “after the fact” procedures, i.e. incident response and dispute resolution. When a user reports that an asset does not work, an event has been cancelled, or a ban is unjustified, smart contracts can hold funds in escrow and release them once the conditions are met. AI can classify requests and prioritise them, which shortens handling time when ticket volumes are high. In more complex cases, arbitration is used (e.g. Kleros) or pre-established DAO policies.

1. 01Hard enforcement mechanismsOn-chain smart contracts, auditable rules.
2. 02Continuous AI analysisDetecting abuse, behaviour and anomalies off-chain.
3. 03Broad risk surfacePhishing in VR, NFT manipulation, AI model vulnerabilities.
4. 04Hybrid smart contract auditAI speeds up the audit, the final decision belongs to the auditor.
5. 05Incident response planningEarly planning, because metaverse errors escalate quickly.

Effective security requires combining hard on-chain rules with intelligent off-chain threat monitoring and real-time incident preparedness.

Community and DAO management in the metaverse

Community and DAO management in the metaverse comes down to moving decisions from “chat chaos” into a process that can be tracked, enforced and audited. In practice, a DAO relies on off-chain voting (e.g. Snapshot) and on-chain execution (e.g. Safe + timelock), so that decisions have a real impact on the world and the economy. AI supports this process operationally: it summarises discussions, flags duplicate proposals and helps delegates understand the consequences of changes. As a result, transparency increases and the barrier to entry for new members falls.

The greatest value of AI in DAOs comes from its role as a “secretary”: analysing proposals, preparing summaries and simulating the effects of changes in the economy (e.g. drop rate, marketplace fees) published as voting reports together with input parameters. This solution makes it easier to assess risk and reduces decisions made “by instinct”, because the results can be compared with one another and critically reviewed. At the same time, communities organise development funding through grant budgeting: the DAO treasury is public, and payouts are delivered in stages via milestones. AI can support contractor risk assessment (delivery history, quality) and spot signs of abandoned initiatives (e.g. no commits, no updates).

To avoid ending up with pure plutocracy, governance implements mechanisms such as quadratic voting, delegation and contribution-based reputation (e.g. POAPs for work, VC for certifications). In this setup, AI helps detect artificial reputation pumping and analyse qualitative contributions (e.g. code review), which supports more “substantive” decisions. In the metaverse, it is also important to define moderation and security policies: the DAO can vote on rules, penalty thresholds and appeal procedures, while AI reports violation statistics (e.g. number of incidents per 1000 sessions) and suggests adjustments based on data. For private guilds and VIP zones, token-gated governance is used, where access and voting rights depend on holding an NFT/POAP, and AI helps choose the rules (e.g. minimum activity) and detect abuse.

Decision transparency in a DAO is based on public votes and logs (on-chain/off-chain) and on an explanatory layer that makes it easier to navigate the history. AI can generate summaries with links to sources (proposals, discussions, results), so new people understand the context and intent of changes more quickly. This reduces the risk of narrative manipulation, because the summary can be compared with the source materials. As a result, world governance becomes more predictable, and rule enforcement is less dependent on manual decisions by individual moderators.

Practical applications across different industries

Practical applications of AI, Web3 and metaverse across industries come down to combining an immersive experience with accountability (ownership, payment, proof of delivery) and automating matching and quality assessment through AI. In virtual retail, metaverse makes it possible to showcase a product in 3D (e.g. shoes, furniture), while AI supports size selection, style and recommendations during interaction with the customer. Web3 can at the same time record proof of purchase and ownership of the digital version, e.g. as an NFT acting as a receipt and a key to limited-edition skins. This makes it possible to connect a purchase in the virtual world with the economy and access rights, without manual “transcribing” of permissions between systems.

In metaverse events, the ticket-as-NFT model works well because it makes it possible to reduce scalping and build premium experiences through transfer control. AI flags unusual purchases, for example repetitive patterns of mass transactions, and the rules recorded in the ticket can take into account transfer limits or commissions on resale. Participants can receive POAPs or dynamic badges for activity during the event, which makes it easier to design benefits available after the event. If fair distribution and “post-participation benefits” matter, it is worth combining NFT tickets with AI-based anomaly detection in purchases and POAP/dynamic badges.

In education and certification in VR, AI assesses task performance in a simulation, for example H&S procedures or machine servicing, and the result can be issued as a VC or NFT certificate for verification without contact with the issuer. In healthcare and therapy supported by VR, AI can personalise exercises, for example exposure for phobias, while Web3 is used for settlements and control of consent over data, with sensitive recordings usually remaining off-chain and only confirmation of the session going on-chain (hash + timestamp). In industry, metaverse with digital twin, for example using NVIDIA Omniverse, supports remote collaboration on a machine model, AI suggests a diagnosis based on logs, and Web3 can record performed maintenance activities as an immutable audit log. In real estate and architecture, metaverse enables immersive presentations, AI generates layout variants and cost estimates, and Web3 provides escrow for deposits and automatic refunds if conditions are not met, for example a missed stage deadline.

Use of AI in creating and moderating content in metaverse

Use of AI in creating and moderating content in metaverse comes down to speeding up the production of a 3D world and enforcing safety rules in a “live” environment. On the creation side, tools such as Blender with AI add-ons, plugins in Autodesk and texture generators, for example Substance 3D with AI features, shorten the time needed to prototype locations and assets. Web3 then makes it possible to immediately tokenise and licence the generated assets when clear rules of use and settlement are needed. Such an approach reduces friction between the creative pipeline and distribution and monetisation in metaverse.

Content and behaviour moderation in metaverse works most effectively when AI detects violations in real time and the system has a mechanism for warnings and penalties that can be audited. AI can identify toxic language, unwanted gestures and breaches of personal space, which helps create safe zones for participants and keep harassment in check. When a project requires transparency of decisions, Web3 can provide an auditable warning and sanction system based on reputation or access gates, including temporary bans enforced by access rules. The most practical pattern is “AI detects, Web3 enforces”: automatic detection of violations plus auditable rules for penalties and appeals.

Content creation also includes the communication layer and support for international experiences, where AI can remove language barriers without involving large teams. ASR/TTS models (e.g. Whisper + TTS) enable real-time transcription and dubbing, which is highly important during events and meetings in VR. Web3 can also settle micropayments for actual usage (e.g. payment per minute of translation) and support management of consent for recording. At the same time, when assets generated by models are created, Web3 makes it possible to record provenance (timestamp, file hash, licence in metadata), and AI can signal infringement risk by comparing similarity with known datasets.