When ransomware, social engineering, and infrastructure exploitation are leveraged at machine speed in cyberattacks, organizations will need the recommendations stated below.

In its yearly analysis of global cyberattack trends, Check Point Software estimates that organizations experienced an average of 1,968 incidents per week in 2025, as attackers increasingly leveraged automation and AI to move faster, scale more easily, and operate across multiple attack surfaces simultaneously.

Apparently, AI has been driving one of the fastest security shifts the industry has experienced, forcing organizations to reassess long-standing assumptions about how attacks originate, spread, and are stopped. Capabilities once limited to highly resourced threat actors are now widely accessible, enabling more personalized, coordinated, and scalable attacks against organizations of all sizes.

Defending against this shift requires revalidating security foundations for the AI era, and stopping threats before they can propagate.

Continual cyber trends ahead

Several industry predictions converge on recognizing the trend of a clear shift towards integrated, multi-channel attack campaigns that combine human deception with machine-speed automation:

• AI-driven attacks will become more autonomous: AI is increasingly embedded across attack workflows, accelerating reconnaissance, social engineering, and operational decision-making.
• Ransomware operations will continue to fragment and scale: The ransomware ecosystem has decentralized into smaller, specialized groups, contributing to a substantial year-over-year increase in extorted victims and a rise in new Ransomware-as-a-Service groups. AI is now being used to accelerate targeting, negotiation, and operational efficiency.
• Social engineering will expands beyond email: Attackers are increasingly coordinating campaigns across email, web, phone, and collaboration platforms. ClickFix techniques have surged by 500%, using fraudulent technical prompts to manipulate users, while phone-based impersonation will evolve into more structured enterprise intrusion attempts. As AI becomes embedded in browsers, SaaS platforms, and collaboration tools, the digital workspace is emerging as a critical trust layer for attackers to exploit.
• Edge and infrastructure weaknesses will increase exposure: Unmonitored edge devices, VPN appliances, and IoT systems are increasingly used as operational relay points to blend into legitimate network traffic.
• New risks will emerge in AI infrastructure: According to some reports, at least 40% of 10,000 Model Context Protocol (MCP) servers may be exposed as AI systems, models and agents become embedded in enterprise environments.

Recommendations for security leaders in 2026

According to Check Point analysts, defending against AI-driven threats requires rethinking how security is designed and enforced, not simply reacting faster. Based on the trends observed, the following measures are worth noting:

• Revalidate security foundations: AI-driven attacks exploit speed, automation, and trust across environments not built for machine-paced threats. Organizations should reassess controls across networks, endpoints, cloud, email, and SASE to stop autonomous, coordinated attacks early.
• Adopt AI adoption securely and responsibly: As AI becomes embedded in daily workflows, blocking its use can increase risk. Security teams should apply governance and visibility to sanctioned and unsanctioned AI usage to reduce exposure from high-risk prompts, data leakage, and misuse.
• Protect digital workspaces: Social engineering now spans email, browsers, collaboration tools, SaaS applications, and voice channels. Security strategies must protect the workspace where human trust and AI-driven automation intersect.
• Harden the Edge and all infrastructure: Unmonitored edge devices, VPN appliances, and IoT systems are increasingly exploited as stealthy entry points. Actively inventorying and securing these assets will help reduce hidden exposure and attacker persistence.
• Adopt a Prevention-First approach: With attacks operating at machine speed, prevention-led security is essential to stop threats before lateral movement; data loss, or extortion can occur.
• Unify visibility across hybrid environments: Consistent visibility and enforcement across on-premises, cloud, and edge environments can reduce blind spots, lower complexity, and strengthen resilience.

Finally, an optimal incident response framework provides invaluable insight into how attacks succeed. Exposure management applies those lessons earlier in the lifecycle, utilizing threat intelligence to identify and mitigate risk before incidents occur.  By connecting pre-incident intelligence with post-incident learnings, organizations can close the gap between what they respond to, and what they prevent. Threat intelligence is not an add-on or a feed; it should be the starting point for understanding exposure, prioritizing action, and reducing the volume of incidents that require a response, according to the analysts.