At Kraken, transparency is not a slogan. That’s standard. Our latest news Preliminary Proof (POR) AuditIt was completed at that point June 30, 2025Again, make sure that the client assets held on the platform are backed up to 1:1 or later. This process includes the main crypto assets BTC, ETH, SOL, USDC, USDT, XRP, ADA.

We don’t expect blind trust. We don’t need it. Provides evidence of encryption.

Is it your first time with POR? Learn how it works in a beginner’s guide.

The June 2025 report shows

Our POR Audit captures a complete snapshot of client assets across all services, not just spot balance. Included Margin accounts, futures holdings, and betting assetsIt provides a full spectrum view of customer exposure.

Reserve ratios as of June 30, 2025

A simple review – What is a proof of preparation?

Proof of reserves is a encryption process that allows the client to independently and personally verify that the assets are contained in a third-party audited snapshot of the platform’s liability.

Use a Markle Tree Combine individual balances into a single cryptographic hash. Client receives a Personalized Markle Proofcan be used to confirm inclusion without revealing personal details. An independent auditor then checks Kraken’s Onchain Holdings exceeds the total client balance – Effectively test your complete preparation without assumptions.

Why is Kraken’s POR going even further?

Nowadays, more exchanges offer some form of “pole” offering, but not all of them offer the same level of rigor or transparency. This is what sets us apart:

1. Consider not only assets but also liabilities

Some platforms show what they have, but skip what they owe. Included Client’s Total Debt With all audits. Below that is not complete evidence of reserves.

2. User-Level Verification

All clients can Check your own inclusion Use the open source Merkle Verification Tool. This is not just about trust, it is about verification.

3. Ten Year Consistency

First I developed POR 2014 – And we haven’t stopped. Kraken will implement for Regular and orderlyit’s not just the news cycle.

POR is essential. It’s not about promises. That’s about evidence – Visible, encryption, and proof of third-party verification. Kraken’s process is built to withstand scrutiny and provide information to users.

What’s next – Enlarge scope

We are committed to publishing Pors quarterin addition to our financial disclosures, we also ensure that users have a normal window in our platform solvency. We are also actively working to expand and include our pole coverage. More supported assetsgives us a broader view of the reserves throughout our ecosystem.

From the beginning, Kraken was a supporter. Accountability, Independence, Cryptographic Primary Values. I believe transparency should be an industry norm, not an afterthought. So we keep raising the bar.

Ready to verify your account? Please visit the spare proof portal.

The following is a guest post and opinion from Felix Xu, Founder of ARPA Network.

Walk into any of Cloudflare’s global offices and you’ll find some unusual decor. In San Francisco, it’s floor-to-ceiling lava lamps, known as “the wall of entropy.” In London, it’s the “unpredictable pendulums.” These aren’t just pretty backdrops—they are grist for the randomness mill, exemplifying the ongoing creative and engineering race for true randomness.

Randomness is the unsung hero of the modern internet—the cornerstone of encryption, the backbone of fair gaming systems, and increasingly, a critical component in AI verification. Yet, as we hurtle toward a future where crypto represents a growing share of the global economy and AI agents gain greater autonomy—particularly over financial operations—the integrity of randomness becomes not just a technical concern but an existential one.

The Myth of Perfect Randomness

Computer scientists have long sought an idealized version of randomness, embodied by the theoretical “random oracle,” a hypothetical black box providing truly unpredictable outputs for every input. Unfortunately, perfect randomness is practically unattainable. Instead, digital systems rely on pseudorandom functions—sophisticated algorithms designed to simulate randomness convincingly. Physical entropy sources, such as Cloudflare’s “wall of entropy” lava lamps or London’s unpredictable pendulums, serve as essential real-world seeds for these pseudorandom functions, introducing genuine unpredictability from natural phenomena into cryptographic processes.

Yet, this blend of physical entropy and pseudorandom algorithms isn’t foolproof. As MIT computer science professor Steve Ward points out, knowing an algorithm and its initial seed can enable prediction of supposedly random outcomes—such as the next card dealt in online poker. Such vulnerabilities underscore the critical importance of genuinely unpredictable and verifiable randomness in technology-dependent contexts, from digital gaming to financial security.

Verifiable Randomness in AI

Artificial Intelligence systems increasingly rely on randomness to ensure fair, unbiased, and robust outcomes, playing an indispensable role across diverse applications—from healthcare diagnostics to financial decision-making. Randomness helps AI models avoid overfitting by introducing necessary variability into training processes, making predictions and decisions more adaptable and reflective of real-world scenarios. However, when randomness is not verifiable, it becomes impossible to ensure that AI-generated outcomes are genuinely impartial and resistant to hidden biases.

Take, for example, AI-driven financial trading algorithms. These systems utilize randomness to explore various investment scenarios and prevent predictable exploitation. However, without transparent and verifiable randomness, financial institutions and regulators cannot confirm that the model’s decisions are truly unbiased. The integrity of randomness directly influences market fairness, as hidden biases could disproportionately favor certain assets or market participants.

Similarly, in generative AI applications such as language models, image generation, or personalized recommendations, the randomness parameter—known as “temperature”—profoundly affects output quality. A low temperature generates consistent yet potentially repetitive outputs, while higher temperatures introduce greater variability but risk unpredictable and unreliable responses. Without verifiable randomness, users and stakeholders cannot confirm that the model’s claimed temperature settings accurately reflect actual operational conditions—creating opportunities for covert biases and subtle manipulation.

Moreover, AI systems used in critical decision-making scenarios—such as autonomous driving or medical diagnosis—must reliably produce unbiased, equitable outcomes. Verifiable randomness ensures stakeholders can independently verify these systems’ fairness, significantly boosting public trust and regulatory compliance.

The Importance of Verifiable Randomness Functions (VRFs)

At its core, verifiable randomness addresses a fundamental paradox in our digital systems: how do we create unpredictability that can nevertheless be trusted? Verifiable randomness rests upon three pillars:

1. Unpredictability: The generated values cannot be anticipated in advance, even by the system’s creators or operators.
2. Bias-resistance: The output distribution contains no detectable patterns or skews that could be exploited.
3. Public verifiability: Anyone can independently confirm that the random values were generated according to the specified protocol, without requiring access to secret information.

Public verifiability distinguishes verifiable randomness from traditional random number generation. It creates a bridge between the necessary chaos of true randomness and the transparency required for trust. It may sound dramatic, but without that trust, insiders could rig AI algorithms and crypto platforms in their favor—plunging our civilization into a technological dark age.

Toward Decentralized Verifiable Randomness

Likewise, in blockchain systems, randomness underpins critical functions including validator selection, transaction ordering, and token distribution. When Ethereum selects validators for block production or when NFT platforms determine rare trait distribution, randomness decides outcomes worth billions. Any manipulation could allow malicious actors to gain unfair advantages, potentially undermining the entire industry.

The “stakes” (no pun intended) are particularly high in Proof-of-Stake systems, where validators are selected probabilistically. If validator selection becomes predictable or manipulable, attackers could corrupt the network by front-running blocks. Similarly, in DeFi, predictable randomness enables flash loan attacks and market manipulations that drain liquidity pools.

The ideal solution combines true randomness with decentralized verification—distributed networks generating collective randomness where no single entity controls the outcome, yet everyone can verify its integrity.

The Path Forward: Embracing Verifiable Randomness

As we entrust AI systems with greater autonomy and responsibility, verifiable randomness becomes not just a technical challenge but a foundational requirement for trusted systems. Organizations developing AI agents must prioritize verifiable randomness before shipping code—or risk catastrophe.

The technology industry stands at a crossroads. We can continue building AI systems and offloading more capital to crypto-systems on the shaky foundation of conventional randomness and opaque decision processes—or we can embrace verifiable randomness as part of a broader commitment to transparency and trust.

Without verifiable randomness, we’re building our future on digital quicksand. With it, we have the conditions for a flourishing future—where our technological superpowers work with us, not against us.