Trump Reverses Biden’s AI and Crypto Security Regulations

On January 23, 2025, President Donald Trump signed Executive Order No. 14179, “Removing Barriers to American Leadership in Artificial Intelligence,” marking a pivotal shift in the U.S. regulatory approach to artificial intelligence (AI) and cryptocurrencies. By repealing President Biden’s 2023 executive order—an order that had established rigorous guardrails for AI safety, compliance, and risk mitigation—Trump’s administration signaled a profound change: prioritize innovation and market leadership over broad federal oversight.

This move is deeply relevant for the cybersecurity and fintech sectors, which are increasingly reliant on AI-driven solutions and automation to defend against rapidly evolving threats. As the U.S. regulatory climate moves from cautious oversight to accelerated innovation, responsibilities for AI cybersecurity automation, compliance, and ethical deployment now shift more squarely onto the shoulders of private sector organizations.

With the appointment of David Sacks as “AI & Crypto Czar” and the launch of the $500 billion ‘Stargate’ project—led by OpenAI, Oracle, SoftBank, and MGX—the U.S. is poised for an AI infrastructure boom. However, this deregulation, while catalyzing economic growth and technological advancement, also raises critical questions: How can organizations ensure the security of digital assets? How can AI be leveraged for intelligent cyber threat mitigation in a rapidly shifting legislative landscape? And how does machine learning cryptocurrency security evolve when compliance guardrails are loosened?

This article explores the technical, regulatory, and practical dimensions of these questions—offering ZeroDai’s perspective on how artificial intelligence threat detection and AI-driven cybersecurity solutions can meet the challenges and opportunities of this new era.

The repeal of Biden’s executive order removes requirements for AI developers to submit safety results, adhere to federal risk standards, and comply with broad oversight mechanisms. Instead, the focus is now on unfettered innovation, with minimal government intervention. For cybersecurity, this means:

• Greater autonomy for AI development – Companies have the freedom to build and deploy advanced AI models without mandated federal safety checks.
• Decentralized compliance – The onus is on businesses to self-regulate and implement robust security protocols.
• Accelerated deployment cycles – With fewer regulatory bottlenecks, organizations can fast-track the adoption of new AI-powered tools for both cybersecurity and crypto asset management.

In this environment, the technical role of AI expands dramatically. AI-powered security systems are uniquely positioned to compensate for regulatory gaps by providing:

1. Automated Threat Detection and Response

Modern AI cybersecurity automation platforms use advanced machine learning and neural networks to monitor network traffic, endpoint behavior, and user activity. By analyzing billions of data points in real time, these systems can:

• Detect zero-day exploits and previously unknown malware strains.
• Identify anomalous transactions in cryptocurrency networks.
• Trigger automated responses (quarantining devices, blocking transfers) within milliseconds.

For example: According to IBM’s 2024 Cost of a Data Breach Report, organizations using AI-driven threat detection tools identified and contained breaches 28% faster than those using traditional methods. In the decentralized world of digital assets, this speed is critical.

2. AI in Digital Asset and Cryptocurrency Security

With deregulation, the crypto industry faces both increased innovation and heightened risk. AI can mitigate these risks by:

• Monitoring blockchain ledgers for suspicious patterns indicating fraud, money laundering, or smart contract exploits.
• Automated crypto threat detection tools that flag wallet addresses associated with ransomware, phishing, or illicit activity.
• Using predictive analytics to forecast emerging attack vectors against decentralized finance (DeFi) protocols.

Case in point: Chainalysis, a leading blockchain analytics company, leverages AI to scan over $1 trillion in annual transactions and has helped recover over $10 billion in stolen digital assets since 2021.

3. Adaptive Security for Fintech

As fintech innovation accelerates, so does the attack surface. AI can deliver:

• Behavioral biometrics to authenticate users based on unique patterns (keystroke dynamics, device usage).
• Continuous risk scoring for transactions, adapting in real time as new threats emerge.
• Automated compliance reporting, helping organizations meet evolving state or industry-specific standards in the absence of federal mandates.

4. Intelligent Cyber Threat Mitigation

AI-powered systems can ingest threat intelligence feeds from around the globe, correlating indicators of compromise (IoCs) with internal telemetry to:

• Preemptively block new malware strains before signature-based solutions are updated.
• Identify and neutralize attacks leveraging AI-generated deepfakes or synthetic identities—an increasing concern in deregulated environments.

Practical Implementation: Real Use Cases in AI Cybersecurity

Example 1: AI-Driven SOC Automation

A major U.S. fintech player, anticipating regulatory rollback, invested in ZeroDai’s AI cybersecurity automation platform to replace manual Security Operations Center (SOC) workflows. The result:

• Incident response times dropped by 40%.
• False positives decreased by 65%, thanks to machine learning models trained on billions of security events.
• 24/7 monitoring ensured by AI agents, reducing the need for expensive human analysts.

Example 2: Automated Crypto Threat Detection

A cryptocurrency exchange faced a surge of fraudulent account creation and transaction laundering attempts. By integrating an automated crypto threat detection engine:

• The exchange flagged and blocked 99.7% of suspicious transactions in real time.
• Customer trust improved, as reported fraud cases dropped by 80% within six months.
• Regulatory audits were passed with zero findings, despite the absence of new federal guidelines.

Example 3: AI in Digital Asset Security for Asset Managers

A digital asset management firm deployed AI-driven cybersecurity solutions to safeguard multi-signature wallets and DeFi investments. The AI monitored smart contract interactions, flagged abnormal withdrawals, and provided continuous risk assessment, enabling:

• Real-time alerts for unauthorized access attempts.
• Automated rollback of anomalous transactions detected within 30 seconds.
• Improved compliance with emerging state-level crypto regulations.

Example 4: Machine Learning Cryptocurrency Security in DeFi

A decentralized finance platform implemented machine learning cryptocurrency security models to detect flash loan attacks—a prevalent threat in DeFi. The AI flagged 95% of attempted exploits, saving the platform millions in potential losses.

Challenges and Solutions: Navigating Technical and Regulatory Obstacles

Challenge 1: Fragmented Regulatory Landscape

With the federal government stepping back, states like California and Colorado are likely to introduce their own AI and crypto standards. This patchwork can create:

• Compliance headaches for organizations operating across multiple jurisdictions.
• Inconsistent security practices, increasing systemic risk.

Solution: Deploy AI-powered compliance automation tools that scan for and adapt to regional regulatory changes. AI can ingest, interpret, and apply new rules dynamically, ensuring continuous compliance.

Challenge 2: Bias and Ethical Risk in AI

The removal of federal bias mitigation requirements increases the risk of ideological or discriminatory outputs from AI models.

Solution: Adopt open-source AI fairness toolkits and integrate bias detection algorithms within AI pipelines. Regular audits—driven by AI—can identify and remediate unintended bias before models are deployed.

Challenge 3: Sophisticated AI-Driven Attacks

Deregulation not only accelerates innovation among defenders but also among adversaries, who may weaponize AI for:

• Automated phishing and social engineering at scale.
• Deepfake-based identity fraud targeting financial institutions.
• AI-crafted malware that evades signature-based defenses.

Solution: Leverage AI-powered adversarial simulation platforms to test defenses against the latest AI-based threats. Implement deep learning models trained to detect synthetic media and abnormal behavioral patterns.

Challenge 4: Secure AI Model Lifecycle Management

Without mandated reporting, organizations may neglect secure model development, leading to vulnerabilities in the AI supply chain.

Solution: Integrate AI-driven model risk management platforms that monitor for data poisoning, model inversion, and unauthorized model access throughout the development lifecycle.

Future and Trends: The Evolution of AI-Driven Cybersecurity

As the U.S. regulatory posture shifts toward deregulation and rapid AI adoption, several trends are set to reshape cybersecurity automation for fintech and digital asset sectors:

1. Explosion of AI-Driven Security Startups

With barriers to entry lowered, expect a surge in innovative startups focused on AI in digital asset security and machine learning cryptocurrency security. These firms will drive new approaches in threat detection, fraud prevention, and risk analytics.

2. Increased Role of Industry-Led Standards

In the absence of federal guidance, consortia such as the Financial Services Information Sharing and Analysis Center (FS-ISAC) and the Crypto Information Sharing and Analysis Center (Crypto-ISAC) will set best practices for AI safety, interoperability, and security.

3. Greater Use of Federated Learning and Privacy-Preserving AI

To manage privacy and compliance risks, organizations will increasingly adopt federated learning—where AI models are trained across decentralized data silos without centralizing sensitive information. This will be crucial for protecting PII and transaction data in the fintech sector.

4. AI-Augmented Human Analysts

Even as AI cybersecurity automation advances, the most effective defense will be a hybrid of AI-augmented human expertise, enabling faster, more accurate incident response and strategic decision-making.

5. Emergence of AI-Powered Crypto Compliance Engines

With state-level regulations proliferating, AI-powered compliance engines will become standard, automating the mapping of business processes to regulatory requirements and providing real-time alerts on compliance gaps.

Statistics highlight the impact: Gartner predicts that by 2026, organizations integrating AI-driven cybersecurity solutions will reduce the average lifecycle of data breaches by 50%, and the global market for AI in cybersecurity is projected to exceed $46 billion by 2027.

Conclusion: ZeroDai’s Call to Lead in the New AI Security Frontier

The repeal of Biden’s AI and crypto regulations represents both a challenge and a historic opportunity. In a landscape where innovation is prioritized over centralized oversight, organizations bear greater responsibility for ensuring the security, fairness, and resilience of their AI-driven systems.

At ZeroDai, we believe that the answer lies in proactive adoption of AI cybersecurity automation, continuous risk monitoring, and industry collaboration. The path forward requires:

• Embedding AI at the core of your cybersecurity strategy.
• Automating threat detection and response across all digital asset platforms.
• Adopting adaptive compliance frameworks powered by AI to navigate evolving regulatory environments.
• Investing in AI-driven ethical and bias mitigation tools to uphold trust and fairness.

As the U.S. enters a new era of AI-driven economic growth, the stakes for security, compliance, and ethical AI have never been higher. ZeroDai invites fintech leaders, CISOs, and digital asset innovators to partner with us—leveraging the most advanced AI-driven cybersecurity solutions to secure the future of finance and technology.

The future belongs to those who can innovate fearlessly—and secure relentlessly. Let’s shape it together with intelligent, automated, and ethical AI security.