AI-Driven Network Threat Landscape: How Enterprises Can Respond to 'Normalization' Attacks and the Risk of Misusing Legitimate Tools

AI-Driven Cyber Threat Landscape: How Enterprises Can Respond to 'Normalized' Attacks and the Risk of Misusing Legitimate Tools

Introduction

In the first half of 2026, the cybersecurity landscape is undergoing a profound paradigm shift. AI technology is no longer a distant future concept but is infiltrating every link in the attack chain, from assisting vulnerability research to enhancing malware stealth, its impact evolving from "visible incidents" to "normalized infiltration." Threat actors exhibit a clear preference: they tend to abuse legitimate tools, trusted platforms, and daily workflows already present in the enterprise environment to achieve intrusion, lateral movement, and monetization. This attack pattern, 'hiding in plain sight,' poses severe challenges to traditional defense models based on technological novelty. This report aims to provide CISOs and security decision-makers with a risk assessment and defense roadmap based on trusted intelligence.

Event Overview

According to Recorded Future's analysis, malicious software and vulnerability exploitation activities in the first half of 2026 show a significant increase in attackers' reliance on existing tools and workflows. The key technical background is that the introduction of AI capabilities is mainly at the stage of enhancing existing intrusion tactics (AIM3 Level 1-3), rather than fully autonomous attacks. Known facts include:

1. Normalization of Vulnerability Exploitation: There are numerous CVEs being actively exploited, many of which allow network access without prior authentication or enable Remote Code Execution (RCE) when combined with network reachability. 2. AI-Augmented Attack Enhancement: AI is being used to assist in malware persistence, user interface interaction, code generation, and the camouflage of attack logic, making malicious activities more deceptive and harder to analyze. 3. Supply Chain and Platform Abuse: Attackers are leveraging AI-driven development tool ecosystems and trusted third-party services to propagate malicious components or credentials, achieving spread across downstream cloud and software ecosystems.

Technical and Risk Analysis

1. Attack Vectors: Exploiting 'Normality' for Infiltration

The most prominent trend among attackers currently is the use of legitimate tools and workflows for activities. This includes, but is not limited to:

• Tool Abuse: Attackers frequently use remote access tools such as PsExec, s5cmd, AnyDesk, as well as internal scripts and management commands, to execute malicious payloads.* Tool Abuse: Attackers frequently use remote access tools such as PsExec, s5cmd, AnyDesk, and internal scripts and management commands to execute malicious payloads. This method makes it difficult for security systems to distinguish malicious activities from normal system administration activities.
• Social Engineering Optimization: The social engineering tactics of ransomware attacks (such as ClickFix-style social engineering) are constantly being optimized to better integrate into daily emails and office workflows.
• Mobile and Payment Security Challenges: Android NFC malware is emerging as a prominent mobile trend, exploiting NFC functionality to steal payment card data or perform replay attacks on contactless transactions, directly threatening the security of mobile users and payment processes.

2. Threat Evolution Empowered by AI

The role of AI in cybersecurity is "augmentative" rather than "revolutionary." AI currently plays a major role in the following areas:

• Accelerating Vulnerability Research: AI-assisted research greatly increases the output of vulnerability reports, accelerating the analysis path for vulnerabilities and the development of exploit code, allowing attackers to convert zero-day vulnerabilities into usable attacks more quickly. For defenders, this means the "window of opportunity" for vulnerabilities is compressed, requiring faster response mechanisms.
• Enhancing Malware Stealth: Malware is beginning to use Generative AI (GenAI) to interpret user interface elements, thereby generating more targeted and persistent scripts and camouflage logic that are harder for traditional static analysis tools to detect.
• Leveraging Delivery Channels: Attackers are using the AI ecosystem (such as GitHub and integrations of various AI tools) as carriers to distribute malware, spreading threats by masquerading as legitimate automated tools or dependencies.

Enterprise Impact Analysis

Enterprise security departments need to shift from technical details to business impact, assessing the profound impact of these trends on operations.## Enterprise Impact Analysis

The enterprise security department needs to shift from technical details to business impact, assessing the deep operational impact of these trends.

• Operational Risk: Attackers exploiting legitimate tools significantly prolong the time to detect threats. Once infiltrated, lateral movement paths become more stealthy, and traditional signature-based detection methods become drastically less efficient. This directly leads to extended security team response times and increased risk of business disruption.
• Data Risk: The threat posed by mobile NFC attacks on sensitive payment data, as well as backdoors planted in the supply chain, can lead to customer privacy breaches and financial losses. The risk of data leakage is no longer just a single event but a normalized infiltration risk.
• Financial Risk: The acceleration of vulnerability exploitation means rising remediation costs and potential fine risks. Simultaneously, the revenue loss from business interruptions is a direct and quantifiable financial impact.
• Compliance Risk: With global tightening of identity and data protection regulations (such as GDPR, CCPA), failing to effectively manage the risks associated with identities and credentials, and failing to promptly patch vulnerabilities being accelerated by AI, will directly lead to severe compliance penalties.

Industry Trend Observations

The AI-driven threat landscape clearly points to a core trend: the focus of defense must shift from "detecting isolated incidents" to "detecting abnormal behavior sequences." The proliferation of AI will not bring a "silver bullet" that can automatically defend against all attacks, but it is changing the "speed" and "stealth" of attacks.

1. Accelerated Implementation of Zero Trust Architecture: Faced with the abuse of legitimate tools, traditional perimeter-based security models are obsolete. The Zero Trust principle—never trust, always verify—is an inevitable direction for countering "insider threats" and "lateral movement." This requires strict identity, device, and context verification for every access request. 2. Extreme Importance of Identity and Credential Governance: Given attackers' appetite for credentials, the widespread adoption of Multi-Factor Authentication (MFA) and the automation of lifecycle management for all identity credentials are the minimum thresholds, but also the highest value defense layers. 3. AI Arms Race in Defense and Offense: Attackers use AI to accelerate vulnerability exploitation and counter defenses; defenders must use AI to accelerate threat intelligence analysis, automated vulnerability risk scoring, and threat hunting to shorten the "detection-to-remediation" loop time.

Defense and Response Recommendations

To cope with this normalized, AI-enhanced threat environment, enterprises need to adopt multi-layered defense strategies.

Enterprise Level * Strengthen Identity Security: Implement strict MFA policies and enforce zero-trust access controls for all developer credentials and high-privilege accounts.## Defense and Response Recommendations

To cope with this normalized, AI-enhanced threat environment, enterprises need to adopt multi-layered defense strategies.

Enterprise Level * Strengthen Identity Security: Implement strict MFA policies and enforce zero-trust access control for all developer credentials and high-privilege accounts. * Refine Risk Management: Establish an end-to-end third-party risk management system and closely monitor the security status of AI tools and SaaS platforms in the supply chain. * Security Governance: Shift security governance from passive compliance to proactive risk-driven management, ensuring that security budgets and resources are prioritized for high-risk identity and vulnerability management areas.

Technical Level * Evolution of XDR/SIEM: Deploy Extended Detection and Response (XDR) platforms capable of correlating endpoint, identity, network, and application data, focusing on rule sets designed to detect "anomalous behavior sequences" (e.g., a legitimate tool being used for unexpected code execution). * Intelligent Vulnerability Management (VM): No longer rely solely on CVSS scores. Combine with threat intelligence to prioritize patching vulnerabilities that have "remote exploitability" and "no authentication requirements." Utilize AI-assisted tools for automated vulnerability enrichment and prioritization. * Behavioral Baseline Modeling: Establish baseline models for user and system behavior, monitoring tool usage patterns and network traffic in real-time. If a deviation from the baseline is detected, immediately trigger isolation and manual review.

SecurityPost Insight

The core implication of the threat landscape revealed in this report for the first half of 2026 is: The focus of security confrontation has shifted from "how technologically advanced can we be" to "how intelligently can we understand and prevent the malicious intent behind the use of legitimate tools." The intervention of AI makes every step of an attack more "deceptive" and "difficult to trace." For enterprises, this means the complexity of security defense will grow exponentially, and single defensive technologies will be insufficient. A successful security strategy must be a highly integrated, identity-centric zero-trust framework, supplemented by the ability to leverage AI to accelerate threat hunting and risk quantification. Enterprises need to transform security teams from "incident responders" into "behavior predictors," continuously investing in building defense systems capable of identifying and interrupting activities at different stages of an attack chain.