Cybercrime 2030: The Death of Carding and the Birth of the Era of Total Digital Hunts

[Professor]

Introduction: The End of the Era of Isolated Threats​

In 2026, carding — the theft of payment card data — could still be identified as a distinct type of cybercrime, with its own tools, markets, and specialists. But it's already clear: by 2030, this narrow specialization will disappear. We're witnessing a massive convergence, where former "carders," "phishers," "rootkit writers," and cryptojackers are merging into a single stream of targeted, adaptive, and multi-stage attacks. Their goal is no longer simply the card number and CVV — it's the complete digital profile and all associated assets.

Part 1: The Anatomy of Transformation – Why Carding Will Die​

1.1 Technological obsolescence​

• Tokenization and one-time data: By 2030, the widespread adoption of dynamic tokens (like Apple Pay, Google Pay) will make static card data useless in real time.
• Biometrics and continuous authentication: Systems will analyze behavior (typing patterns, cursor movements, usage patterns) 24/7, rather than requiring a one-time password or CVV.
• Next-generation cryptography: The introduction of post-quantum cryptography in payment systems will completely undermine classical methods of data interception.

1.2 The Economics of Cybercrime: A Paradigm Shift​

• Falling profitability: The risk/reward of isolated carding is becoming unprofitable. A single successful data exfiltration of a card with card data costs $5-$20, while comprehensive access to a corporate network can bring in tens of thousands.
• Consolidation of the "shadow market": The closure of large darknet marketplaces (like Joker's Stash) is leading to consolidation of players. Only highly organized groups offering full-service CyberCrime-as-a-Service (CaaS) remain.

1.3 Attack Vector Confluence: 2027 Example​

Imagine an attack on a wealthy user:

1. Stage 1 – Intelligence: The AI system analyzes social media, data leaks, and lifestyle.
2. Stage 2 - Compromise: Through a vulnerability in a smart home or IoT device, an attacker gains access to the home network.
3. Stage 3 – Escalation: Theft of session tokens from a personal laptop, access to a personal bank account, crypto wallets, and a brokerage account.
4. Stage 4 – Monetization:
   • Transfer funds through legitimate payment gateways (card is just one of the channels).
   • Taking out a loan in the victim's name.
   • Theft and sale of personal data for deepfaking or blackmail.
   • The theft of card data is just a by-product, not the goal.

Part 2: The New Cybercrime Architecture by 2030​

2.1 Unified Attack Lifecycle (Cyber Kill Chain 2.0)​

Older models (like the Lockheed Martin Cyber Kill Chain) are being simplified and automated:

1. AI Targeting: Systems scan the internet for potential victims, assessing their "digital capital."
2. Automated exploit development: Based on vulnerabilities in the target ecosystem (not a single device).
3. Stealthy penetration: Through legitimate services (cloud storage, CDN, service APIs).
4. Lateral movement: Within the victim's digital ecosystem (from smartwatch to banking app to brokerage account).
5. Polymorphic Impact: Simultaneously attacking different assets to disguise the true target.
6. DeFi Laundering: Automatically converting stolen assets into private cryptocurrencies or legitimate goods through decentralized finance.

2.2 Key Goals of the New Era​

• Digital identities: Single Sign-On, government IDs on smartphones, biometric profiles.
• Tokenized assets: Real estate, stocks, and art on the blockchain.
• Metaverses and digital property: Unique virtual objects, land in metaverses, collectible NFTs.
• Personal data as capital: Health history, genetic data, behavior patterns – for blackmail, insurance, or targeted advertising.

2.3 Organizational structure of cyber groups​

• Complete decentralization: Instead of hierarchical groups, there are distributed autonomous organizations (DAOs), where the performers do not know the customer.
• Specialization by stages, not by attack types: There are specialists in initial access, privilege escalation, and laundering, but not "carders" or "phishers".
• Legal camouflage: Groups disguise themselves as IT startups and operate in regions with weak extradition laws.

Part 3: Implications for Business and Society​

3.1 Perimeter Security Collapse​

"Castle and moat" protection (firewalls, antiviruses) will become completely obsolete. Security will become:

• Adaptive and continuous: Continuous authentication and behavior analysis.
• Decentralized: Zero-Trust architecture where every request is verified.
• Predictive: AI predicts attack vectors before they occur.

3.2 Legal and Insurance Landscape​

• Cyber incident insurance will become mandatory, but expensive. Insurers will require the implementation of specific technologies.
• International regulation: Global standards for digital identification and cross-border prosecution of cybercriminals will emerge.
• Criminalization of failure to report: Companies will be subject to criminal liability for concealing large-scale leaks.

3.3 Psychological Aspect: Digital Fatalism?​

• Threat fatigue: Users may become less vigilant due to a constant barrage of alerts.
• Trust in Autonomous Defense Systems: Shifting Responsibility to AI.
• The Rise of the Digital Hygiene Market: Personal Security Concierges for the Wealthy.

Conclusion: New Reality​

By 2030, we won't even think of "carding" as a separate phenomenon. Just as today we don't single out "intercom code theft" as a separate crime.

The future belongs to contextual, proactive, and ecosystem-based security. Threats will be assessed not by the type of attack ("is it phishing" or "is it carding"), but by the level of risk to digital capital: "Your digital profile is at risk of being compromised with an 87% probability, with potential losses of $X."

Paradox: The more specialized cybercriminals are (by attack type), the faster they will lose. Those who think in ecosystems, not in terms of individual vulnerabilities, will win. The battle will shift from "technology versus technology" to "AI versus AI," where speed of decision-making and adaptability will be decisive.

The question remains: will law enforcement and regulators be able to transform as quickly as those they are hunting?