Addressing AI regulation gaps and security risks

Regulating AI: Can Self‑Governance Bridge the GRC Gap?

AI is evolving in a regulatory vacuum. The EU AI Act won’t fully enforce high-risk rules until 2027, and most jurisdictions remain tentative. Frameworks like ISO 27001 or SOC 2 are valuable, but weren’t built for AI risks such as data poisoning and prompt injection. The gap between today’s regulation and AI’s demands is widening.

The question is whether self-governance can credibly bridge this gap while addressing GRC challenges:

• Governance: Who is accountable when AI fails?
• Risk: How do we assess threats like bias or adversarial prompts?
• Compliance: How do we prove controls work as models evolve?

Self-Governance as the Path to AI Leadership

At Hacken, we propose a multi-layered control system as the core of self-governance. Traditional frameworks like ISO 27001 and SOC 2 provide the foundation; AI-specific standards such as NIST AI RMF and ISO 42001 introduce lifecycle management and explainability; and voluntary transparency measures, from model cards to red-teaming reports, act as trust-builders.

To make these controls effective, organizations also need formal oversight, with an AI Compliance Officer treating datasets, training pipelines, and model releases as governance assets, as well as holistic risk mapping that documents AI-specific threats and links them to concrete controls.

External Frameworks as Anchors

Self-governance is strongest when it mirrors current regulatory requirements:

• NIST AI RMF offers a structured lifecycle (Govern → Map → Measure → Manage) that gives organizations both vocabulary and methodology for AI governance.
• ISO/IEC 42001, the world’s first certifiable AI governance system, embeds lifecycle and ethical considerations into auditable requirements.

The EU AI Act sets the benchmark, introducing risk tiers, red-teaming, logging, transparency, and human-in-the-loop expectations.

GRC Blueprint for AI Self-Governance

1. Governance Foundation

• Expand existing ISO 27001 scopes to cover LLMs, datasets, and training pipelines.
  
• Define clear policies on data use, model explainability, and monitoring.

2. Risk Management & Controls

• Maintain threat models that explicitly cover prompt injection, poisoning, and bias.
  
• Implement compensating controls such as output validation, bias detection, and throttling.

3. Standards Alignment

• Adopt NIST AI RMF and ISO 42001 for structured governance.
  
• Map controls to anticipated EU AI Act requirements.

4. Operational Resilience

• Secure the MLOps pipeline with hardened endpoints, audit trails, and anomaly detection.
  
• Publish transparency artifacts like model cards or red-teaming reports.

5. Trust & Continuous Improvement

• Use ISO/SOC2 attestation to prove foundational security.
  
• Supplement with voluntary audits and third-party validation.

The Smartest Way to Self-Govern Your AI Journey

Waiting for regulators is not a strategy. Self-governance can bridge the GRC gap by adopting standards early, documenting controls, and stress-testing against real risks. Yet few organizations can design effective AI governance alone. This is where Hacken adds unique value: we’ve helped clients navigate unregulated frontiers for eight years in blockchain security. Done right, compliance shifts from burden to advantage.

AI in Web3: Risks, Controls, and the Road to Verifiable Outputs

AI Security Risks

AI is a force multiplier in Web3—for developers and hackers alike. Deepfakes, voice clones, and LLM-polished outreach amplify phishing, grant fraud, and DAO manipulation—human trust can now be faked in 4K. Prompt injection and agent hijacking let hostile inputs steer agents unless sandboxed with strict permissions. Oracles and data pipelines become high-value targets: poisoned inputs can shift on-chain outcomes without touching the chain. Supply chain risks—backdoored weights, swapped models, or stolen keys—can bias results or divert assets. AI also fuels MEV, front-running, and thin-liquidity manipulation, while the determinism gap persists: AI is stochastic, blockchains are not.

Web3 x AI in the Near Future

We move from impressive AI to provable AI. Off-chain oracles and coprocessors will return proofs: small models via zero-knowledge, heavier inference via trusted hardware or diverse committees. High-impact outputs should carry verifiable receipts before altering on-chain state.

Security economics follows. Restaking will scaffold decentralized AI services—scoring, inference, monitoring—run as opt-in “verifiable services” with slashing for misbehavior. Over time, expect on-chain SLAs: uptime, correctness, and response windows tied to model and dataset IDs.

AI as Core Infrastructure in Web3

AI is now the core Web3 infrastructure. It flags code risks faster than humans, guides auditors to critical logic, and powers monitoring and AML with graph analytics that surface illicit patterns in minutes. For oracles, AI cross-checks sources, scores confidence, and quarantines suspect signals before they hit contracts. In incidents, it clusters phishing domains and traces funds, turning noise into clear action. Developers gain speed with AI that drafts tests, refactors code, and highlights gas hot spots—guarded by CI and human review. The result: an evolving partnership where AI accelerates Web3 without losing accountability.

Decentralized AI

DeAI runs on decentralized infrastructure secured by Web3 primitives, shifting from “trust one provider” to auditable intelligence. Training stays off-chain, but results return on-chain only after verification—via zero-knowledge proofs, hardware attestations, or multi-party approvals.Risks mirror broader AI security: poisoned data, tampered models, collusion, or over-permissioned agents. Effective controls treat AI systems as critical infrastructure: diversify providers, red-team before launch, enforce least-privilege with human checks, and maintain a transparent audit trail.

About Hacken

Hacken is a blockchain security and compliance partner digital asset leaders. Trusted by 1,500+ teams since 2017, we deliver audits, penetration testing, real-time monitoring, bug bounties, and compliance aligned to ISO, SOC 2, CCSS, and CASP. With AI-powered offensive security and enterprise-grade quality, we secure digital assets across Europe, North America, and MENA.

AI and Web3 are moving faster than rulebooks. In moments like this, trust must be engineered, not assumed. Security isn’t a one-time check; it’s a continuous, verifiable commitment.

In the following pieces, we outline a path from box-ticking to proactive governance: closing the AI GRC gap through self-governance and securing the AI–Web3 stack with provable outputs, risk detection, and day-to-day controls.

– Yevheniia Broshevan, CEO &  Co-Founder of Hacken