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AI
How to find out if your AI vendor is a security risk

One of the most pressing concerns with AI adoption is data leakage. Consider this: An employee logs into their favorite AI chatbot, pastes sensitive corporate data, and asks …

large language models
Excessive agency in LLMs: The growing risk of unchecked autonomy

For an AI agent to “think” and act autonomously, it must be granted agency; that is, it must be allowed to integrate with other systems, read and analyze data, and have …

patch
April 2025 Patch Tuesday forecast: More AI security introduced by Microsoft

Microsoft is continuing to build on their AI cybersecurity strategy and this month announced the introduction of new agents in Microsoft Security Copilot. They are introducing …

strategy
7 ways to get C-suite buy-in on that new cybersecurity tool

You’re in the middle of a sprint, juggling deadlines, debugging code, fine-tuning pipelines, and then it happens—you stumble across the perfect cybersecurity tool. It promises …

world map
How to map and manage your cyber attack surface with EASM

In today’s digital landscape, understanding your organization’s attack surface is crucial for maintaining robust cybersecurity. To effectively manage and mitigate …

internet
How to build an effective cybersecurity simulation

Most people groan at the prospect of security training. It’s typically delivered through dull online videos or uninspiring exercises that fail to capture real-world urgency. …

AI
Two things you need in place to successfully adopt AI

Organizations should not shy away from taking advantage of AI tools, but they need to find the right balance between maximizing efficiency and mitigating organizational risk. …

crypto wallet
Android financial threats: What businesses need to know to protect themselves and their customers

The rise of mobile banking has changed how businesses and customers interact. It brought about increased convenience and efficiency, but has also opened new doors for …

AI
A CISO’s guide to securing AI models

In AI applications, machine learning (ML) models are the core decision-making engines that drive predictions, recommendations, and autonomous actions. Unlike traditional IT …

data analytics
Spring clean your security data: The case for cybersecurity data hygiene

Spring cleaning isn’t just for your closets; security teams should take the same approach to their security operations data, where years of unchecked log growth have created a …

identity
The hidden risk in SaaS: Why companies need a digital identity exit strategy

In the face of sudden trade restrictions, sanctions, or policy shifts, relying on SaaS providers outside your region for identity services is a gamble that companies can no …

breach
5 pitfalls that can delay cyber incident response and recovery

The responsibility of cyber incident response falls squarely on the shoulders of the CISO. And many CISOs invest heavily in technical response procedures, tabletop exercises …

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