Europe is seeing an increase in cybercrime and nation-state attacks.

Ransomware and extortion attempts are becoming more frequent, and attackers are targeting both private companies and critical infrastructure.

Extortion and Ransomware Trends
Financially motivated attacks are on the rise. Attackers are using ransomware, business email compromise, and other schemes to demand payments.

Companies need to strengthen detection and response to reduce impact.

Nation-State Activity
Government-backed hackers are intensifying operations in Europe. These attacks often focus on intelligence gathering, critical infrastructure disruption, and espionage.

Security teams must be vigilant and prioritize high-value targets.

Key Sectors at Risk
Industries like healthcare, finance, and energy are most affected.

These sectors face both operational and reputational risks if security is not maintained at a high standard.

Security Recommendations
CISOs should enhance visibility across networks, implement strong endpoint protection, and conduct regular threat assessments.

Collaboration with industry peers and information sharing can improve overall defense posture.

Cyber threats in Europe are increasing in scale and sophistication, making proactive defense and risk management essential for organizations.

The discussion provides a strategic reframing of log pipelines, moving their perceived value far beyond the common "reduce the SIEM bill" narrative.

Balazs Scheidler argues that modern, observable pipelines are critical infrastructure for data quality, classification, normalization, and management, which legacy tools and SIEMs are unequipped or disincentivized to handle.

The central thesis is that the industry's failure to solve basic data quality, parsing, and schema issues (illustrated by a story of corrupted Palo Alto CEF logs being ingested for years) has rendered many detections useless.

Pipelines are the only component incentivized to fix this "data quality gap."

AI is transforming how organizations manage risk. It helps shift security and compliance from reactive responses to proactive, data-driven strategies.

Key Areas of Impact
AI can improve risk identification by spotting patterns and anomalies in large data sets. It can assess risks with predictive models, simulate scenarios, and refine decision-making.

For mitigation, AI suggests controls and strategies in near real-time. Continuous monitoring and reporting help detect unusual activity and streamline compliance processes.

Use Cases Across the Enterprise
In cybersecurity, AI can monitor networks and user behavior to predict attacks. It can summarize regulatory changes, aiding compliance teams.

In quality assurance, AI assists with large-scale reviews, improving accuracy and efficiency.

Challenges in Adoption
Implementing AI requires organizational commitment and skilled personnel. Over-reliance on AI without human oversight can lead to misjudgments. Legacy systems and fragmented security tools can limit effectiveness.

Using third-party AI solutions adds complexity and risk, necessitating contingency planning.

Frameworks and Governance
Successful AI risk management should be integrated into enterprise risk frameworks. Structured governance aligning legal, technical, and cybersecurity teams is essential.

Standards like Google’s Secure AI Framework, NIST AI RMF, and ISO guidelines help guide adoption and oversight.

AI offers powerful tools for proactive risk management, but it must be carefully governed and balanced with human judgment to maximize benefits.

Many companies are struggling to manage how employees use AI tools. While AI adoption is high, policies often lag behind, and unsanctioned tools pose risks to sensitive data and compliance.

Employee Behavior vs. Policy
A survey shows 73% of employees are encouraged to use AI at work, but 37% admit they sometimes ignore company rules.

Many also use AI tools not approved by IT, creating security gaps and potential data exposure.

Governance and Risk Management
IT teams need a clear inventory of AI tools, strong usage policies, and technological controls. Only approved tools should access company data.

Shadow AI can leak sensitive information or act maliciously if unchecked.

Tools to Close the Gap
Solutions like 1Password’s Trelica and Device Trust help organizations monitor AI usage, enforce policies, and guide employees toward approved tools.

These systems provide alerts, block unsafe apps, and educate users in plain language.

Balancing productivity with security requires actively managing AI adoption and closing gaps between policy and actual use.

AI Changes How Code is Secured

AI is now part of software development, and it changes how security teams must work. Many organizations are using or testing AI coding tools, but full visibility into their use is still limited.

This creates new risks that need attention.

Shadow AI Adds Risk

Shadow AI—tools used without full oversight—can hide vulnerabilities. Security teams must understand where AI is used in the software lifecycle to reduce exposure and prevent breaches.

Balancing Speed and Security

As AI coding becomes common, teams face pressure to move fast while keeping code safe. Organizations are shifting from separate security controls to combined platforms that support both speed and protection.

Budget and Planning

Security budgets are expected to rise in 2026 to handle AI-related risks. Investing in AI-aware application security strategies will be key for organizations to stay protected.

The rise of AI in software creates both opportunities and new risks, requiring stronger oversight, smarter tools, and careful planning to keep code secure.

The Rhysida ransomware gang is using fake download pages for software like Microsoft Teams, Zoom, and PuTTY to trick users into downloading malware.

They place these pages as paid ads on Bing, making them highly visible to potential victims.

OysterLoader: The First Step

The malware, called OysterLoader, serves as an initial access tool. Once installed, it opens a pathway for more persistent backdoors, allowing attackers to maintain long-term access to compromised systems.

Exploiting Trust to Bypass Security

Rhysida takes advantage of Microsoft’s trusted code-signing certificates to make their malicious files appear legitimate.

These certificates automatically gain trust within Windows and many security tools, letting the malware slip past defenses unnoticed.

Evasion Techniques

The gang also packages their malware to compress, encrypt, or obscure its functions. This reduces the chances of detection when the malware first appears, making early defense harder for organizations.

Conclusion

Even trusted channels like Microsoft-signed files can be exploited, highlighting the need for constant vigilance and layered security measures to detect and stop sophisticated ransomware attacks.