In a comprehensive cybersecurity forecast released on December 17, 2024, cybersecurity firm HUMAN has projected global losses from cybercrime to reach $12 trillion in 2025, according to Forrester research data. The report, published just two days ago, outlines how artificial intelligence is transforming automated attacks into sophisticated digital fraud operations.
The analysis reveals a significant shift in cyber threat patterns, with web scraping attacks increasing from 30% to 36% of traffic over the past year. According to HUMAN's data, one in five visits to login pages currently represents an attempt to breach user accounts.
The convergence of artificial intelligence capabilities with traditional cyber threats has created conditions for unprecedented attacks. "As artificial intelligence democratizes attack development, speed, and complexity, HUMAN is observing the rise of botnets capable of launching coordinated attacks at a massive and dynamic scale," states Stu Solomon, who authored the December 17 report.
The transformation of attack methodologies represents a fundamental change in how cybercriminals operate. Attacks that previously required days or weeks to retool can now be modified within minutes using AI-aided systems. The technology enables real-time mutation and adaptation of attack patterns to evade detection mechanisms.
Digital advertising faces particular challenges as the sector continues its post-pandemic recovery. The report identifies made-for-advertising (MFA) sites as an escalating threat, with artificial intelligence enabling rapid creation of fraudulent sites that outpace traditional detection methods. This proliferation of artificial ad inventory threatens to diminish market quality while creating additional opportunities for fraud.
The analysis highlights the increasing sophistication of malicious landing pages. Cybercriminals now employ AI-powered tools to mass-produce and test multiple versions of scam pages and "shield" pages - legitimate-appearing sites that conceal malicious content from security systems. These tools optimize conversion rates while maintaining effectiveness at evading detection.
Web scraping presents a complex challenge for content publishers. The documented increase from 30% to 36% in scraping attacks over the previous year indicates an accelerating trend as large language models and AI systems advance their crawling methodologies. This development necessitates sophisticated solutions to differentiate between legitimate scraping activities, such as search engine crawlers, and malicious operations.
Account security faces dual threats from both takeover attempts and synthetic identity creation. The current rate of one in five login page visits representing attack attempts is expected to increase as artificial intelligence enhances credential stuffing and cracking techniques. Traditional CAPTCHA systems have proven increasingly ineffective against these advanced threats.
High-value targets for account takeover attacks include:
- Financial transaction capabilities
- Stored payment methods
- Loyalty programs and rewards
- Premium service access
- Privileged data storage
- Account opening incentives
The landscape of AI chat platforms is undergoing significant changes. The integration of paid search and advertising into chat results, supplementing existing subscription models, creates new vectors for sophisticated fraud attempts.
Looking toward technical defensive capabilities, organizations require comprehensive protection across customer digital journeys. The implementation of adaptive machine learning and real-time threat intelligence has become essential for maintaining transaction authenticity and security.
HUMAN's analysis indicates that successful defense strategies must incorporate:
- Enhanced detection mechanisms
- Modular security solutions
- Advanced monitoring dashboards
- Robust architectural frameworks
- Extensive technology partnerships
The increasing sophistication of automated threats necessitates a corresponding advancement in defense mechanisms. The distinction between human and automated traffic continues to blur, requiring more nuanced and capable security systems.
Financial implications extend beyond direct losses. The report indicates that fraudulent activities impact operational efficiency, customer trust, and market dynamics across multiple sectors. The convergence of various attack methodologies creates compound threats that traditional security measures struggle to address.
The technical architecture of modern attacks demonstrates increasing complexity. Artificial intelligence enables attackers to:
- Generate convincing synthetic identities
- Coordinate distributed attack patterns
- Adapt tactics in response to defensive measures
- Scale operations with minimal human intervention
- Optimize attack efficiency through automated testing
These capabilities present significant challenges for conventional security frameworks. The integration of artificial intelligence in defense systems has become essential rather than optional, particularly in identifying and responding to sophisticated attack patterns.
The acceleration of digital transformation across organizations has inadvertently expanded attack surfaces. This expansion, combined with decreased operating costs for attack tools, has improved the return on investment for cybercrime operations. The economic incentives for automated attacks have increased substantially.
Solomon's analysis emphasizes the critical nature of the current security landscape. The projected $12 trillion in global losses represents an unprecedented scale of potential economic impact. This figure encompasses direct financial losses, operational disruptions, and broader economic effects across multiple sectors and regions.
The report concludes that the effectiveness of cybersecurity measures in 2025 will depend on the ability to implement adaptive, intelligent defense systems capable of matching the sophistication and scale of AI-enhanced threats. The integration of advanced detection capabilities with comprehensive protection frameworks represents a fundamental requirement for maintaining digital security.
These findings indicate a critical juncture in cybersecurity, where the sophistication of defensive measures must evolve to match increasingly capable automated threats. The technical complexity of modern attacks requires correspondingly advanced security solutions, implementing artificial intelligence and machine learning capabilities to maintain effective defense postures.