Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

As 2024 draws to a close, I’m reflecting on a year that has been nothing short of transformative for Appknox. This was a year of bold ideas, breakthrough innovation, and meaningful partnerships. It was a year where we didn’t just meet expectations—we redefined them. In many ways, 2024 was about going back to the fundamentals of why we exist: to make mobile application security simple, scalable, and effective.

As C-suites and boards are bombarded with headlines about AI revolutionizing cybersecurity, it’s no wonder they’re putting pressure on SOC leaders to adopt AI. The promise of AI in the SOC is rightfully alluring. An AI-native autonomous SOC has the potential to create a world where AI Agents collaborate with each other to take care of repetitive tasks and handle the majority of low-level alerts, freeing your human team up for strategic, proactive work. The hurdle?

AI systems bring transformative capabilities to industries like healthcare but introduce unique challenges in protecting patient data. Unlike traditional applications, AI systems rely on conversational interfaces and large datasets to train, test, and optimize performance, often including sensitive patient information. AI systems pose complex risks to patient data privacy and AI data security that cannot be effectively managed using traditional methods.

The rise in cyberattacks has become a pressing concern for organizations worldwide, threatening sensitive data, operational continuity, and trust. Cybercriminals are using increasingly complex strategies to attack vulnerabilities in systems and networks in a variety of organizations. By offering safe, segregated virtual networks in the cloud, Amazon Virtual Private Cloud (Amazon VPC) provides a strong defense against these attacks.

As APIs continue to drive modern digital ecosystems, securing them has become an organizational imperative. Few companies turn to API security testing products to identify vulnerabilities and safeguard their APIs. However, these tools are counterproductive when relied upon as a sole security measure. Here’s why.

The cloud gives you agility, speed, and flexibility – but it also opens new doors for attackers. For DevOps teams, every line of code, every container, and every deployment pipeline is a potential entry point and missteps are easier than ever. Misconfigurations alone cause 80% of all security breaches in cloud environments, so the stakes are even higher. This poses a severe security risk with wide-ranging consequences, making it evident that cloud-native environments demand a new security mindset.

In my experience, computers are only as smart as the person in front of them. Same with AI. The results are dependent on the prompts given. Today, users typing prompts from their brains into Microsoft Security Copilot may find it hard to get value. Prompts with adequate specificity are difficult to create, let alone repeat.

In this guest post, Eric Newcomer, Principal Analyst at Intellyx explores the practical applications and limitations of generative AI. Generative AI is a game-changing technology. Chat bots seem like magic compared to a traditional static web search. You submit questions in natural human language and receive back complete sentences and paragraphs. But it isn’t always clear what it is really good for, given limitations such as hallucinations and inaccurate answers, and possible bias.

Organizations of all sizes require security tools to meet their complex hybrid cloud needs. As their cloud environments and workloads evolve, this includes solutions that can scan for vulnerabilities in container images regardless of their location across public and private cloud environments. The problem is, most organizations lack this capability. Many use tools that don’t allow the flexibility to move quickly and scan their full breadth of cloud assets.