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If you’ve been following along in this series, you already know that Artificial Intelligence (AI) has emerged as a powerful tool for organizations to strengthen their cybersecurity defenses. But how is AI being used in cybersecurity today, and what are its key benefits?
The Gartner® Market Guide for Network Detection and Response sheds light on the key trends and recommendations for security and risk management leaders looking to leverage NDR capabilities. MixMode was listed as a representative vendor within the market guide, offering advanced AI capabilities that enhance network detection and response.
We have now reached the Third wave of AI: Contextual Reasoning. This revolutionary approach, pioneered by MixMode in cybersecurity, utilizes self-supervised and explainable AI to independently learn and adapt without reliance on rules or training data.
There’s bad news and not-so-bad news in the world of cybersecurity. In a recent report, Google revealed a significant rise in zero-day attacks, those exploiting previously unknown software vulnerabilities. However, despite the increase, there’s a silver lining.
While a significant portion (53%) of respondents acknowledge their organization’s early-stage adoption of AI, only 18% report full deployment into integrated security programs. This cautious approach reflects the need for careful planning and implementation to ensure AI enhances, rather than hinders, security posture.
The cybersecurity landscape is under siege. Organizations are bombarded by a relentless barrage of security alerts, often exceeding a staggering 22,111 per week on average. While Artificial Intelligence (AI) has emerged as a powerful tool to manage this overwhelming volume, its effectiveness isn’t without limitations, as vendors flood the market with false advertising and promises.
This week, we’re looking into how the rise of artificial intelligence (AI) has significantly altered the threat landscape, introducing challenges and opportunities for organizations seeking to safeguard their data and infrastructure.
The terms “machine learning” and “artificial intelligence” are frequently used in cybersecurity, often interchangeably, leading to confusion about their precise meanings and applications. Both machine learning and artificial intelligence play pivotal roles in fortifying cybersecurity defenses, yet they encompass distinct methodologies and applications. What are the disparities between them? And how do these technologies converge to bolster cyber resilience?
A new report from Microsoft and Open AI shows how attackers are increasingly using artificial intelligence (AI) to improve their cyberattacks. The report found that nation-backed groups use LLMs for research, scripting, and phishing emails.
Despite AI’s potential to help defend against cyber attacks, AI adoption in cybersecurity practices remains in its early stages. Why is this the case, and how can organizations overcome these hurdles to pave the way for a secure future?
As cyber threats grow more sophisticated, organizations are starting to turn to artificial intelligence (AI) to reinforce their security operations. But how widespread is the adoption of AI, and how effectively is it being leveraged?
Organizations continue to face increasingly sophisticated cyber threats that require a proactive and adaptive approach to cybersecurity. As the volume and complexity of security data grows, security professionals are turning to advanced AI technologies to enhance their capabilities and stay ahead of emerging threats.
Hackathon events like Pwn2Own Automotive 2024 play a crucial role in shedding light on zero-day attacks and significantly benefit security teams in several ways. These events bring together talented security researchers, ethical hackers, and industry professionals to uncover and demonstrate vulnerabilities in automotive technologies, including zero-day exploits.
As organizations grapple with the limitations of legacy security controls in the face of increasingly sophisticated cyber threats, integrating AI-driven threat detection and response capabilities presents a compelling opportunity to bolster their defenses.
The emergence of AI-generated attacks represents a paradigm shift in the cyber threat landscape, posing unprecedented challenges to traditional security solutions. Legacy solutions struggle to contend with intelligent attack automation techniques employed by threat actors, and the overwhelming flood of new malware samples and indicators makes it harder to defend against these attacks.
As we step into 2024, the world of cybersecurity is poised for significant changes. With the rapid evolution of technology and AI, the threat landscape is constantly shifting, presenting new challenges for individuals and organizations alike.
The history of cybersecurity is a tale of constant evolution, marked by the rise and fall of various defense mechanisms in the face of ever-changing threats. Security tools such as antivirus software and firewalls have long been the stalwarts of cyber defense, forming the bedrock of protection for organizations worldwide. While effective in their time, …
Gartner recently released its report Emerging Tech: Top Use Cases for AI in Threat Detection, Investigation, and Response, highlighting how security leaders utilize artificial intelligence (AI) to enhance detection, streamline investigations, and automate response to cyber threats. With increasingly frequent and sophisticated attacks, AI represents a crucial opportunity to augment human security teams.
AI-driven threat detection and response solutions can significantly enhance the defense against ransomware attacks. These solutions provide advanced capabilities for detecting, analyzing, and responding to ransomware attacks.
Defense-in-depth is a cybersecurity strategy that emphasizes deploying multiple layers of security controls and countermeasures to protect critical assets and mitigate the impact of potential attacks.