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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.
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?
Attention, fellow news anchors and concerned citizens! Rob Burgundy is here to tackle a story hotter than a disco inferno in polyester pants: Artificial Intelligence (AI) and Cybersecurity. That’s right, folks. In this digital age, hackers are running rampant like greased weasels in a chicken coop, stealing our precious data faster than you can say “glass case of emotion.” But fear not, for organizations are turning to a new weapon in this cyber war: AI, the thinking machine!
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.
One of the key areas that the Gartner report highlights is AI Enhanced Security Operations. The research observes that most AI development in security products so far has focused on detecting threats, but there is increasing potential to use AI to improve security operations and incident response.
On October 30, 2023, the White House issued an Executive Order promoting safe, secure, and trustworthy artificial intelligence (AI) deployment. This Executive Order recognizes the global challenges and opportunities presented by AI and emphasizes the need for collaboration, standards development, and responsible government use for national security.
Gartner just released its Emerging Tech Impact Radar: Security, which looked at technologies that could help organizations effectively detect and respond to attacks and create better efficiencies through AI-based security hyper-automation.
Several AI approaches are used in cybersecurity, but it’s hard to make sense of the noise, especially when vendors say the same thing. In our newest eBook, we’ll explore what they are and the pros and cons of each one.
Security leaders want to solve problems identifying and understanding anomalies or account access behaviors by correlating anomalous behaviors of specific accounts with other parameters like geography or ingress and egress points, but few rules-based Cybersecurity tools have the ability to do that without a great deal of manual data massaging and manipulating.
Overall, Black Hat 2023 demonstrated that the cybersecurity landscape continues to evolve rapidly. The event highlighted both the threats posed by emerging technologies like AI, as well as the potential for new defensive techniques.
A briefing by the FBI’s Counterintelligence Division highlights the massive potential of artificial intelligence (AI) for advancing cybersecurity and the looming risks of adversaries weaponizing AI for attacks.
The constant threats posed by hackers, state-sponsored cyber attacks, and malicious actors have prompted the creation of cutting-edge strategies to fortify our nation’s cyber defenses. One such response to this growing concern comes in the form of an annual large-scale international cybersecurity exercise.
One of the key predictions is that by 2027, 50% of chief information security officers (CISOs) will adopt human-centric design practices in their cybersecurity programs to minimize operational friction and maximize control adoption. This approach focuses on designing security controls around individuals rather than technology or threats. It recognizes that employees play a crucial role in cybersecurity and aims to reduce the likelihood of risky behavior.
With the Cloud Era, the IT asset estate has expanded, and there is a diverse set of attack surfaces to defend. The term “attack surface” describes the growing IT asset estate, but there is no parallel term to describe where we can detect or respond to attacker activity.