Aqua Cloud Native Blog › Tags: AI Security

TL;DR Security teams have spent years and billions of dollars shifting security left. This has changed what teams can see and introduced a new level of visibility, but it has not helped with what they can control. Last year, more than 48,000 new vulnerabilities were cataloged, growing at roughly 20 percent annually. Add to that …

TL;DR Anthropic recently launched Claude Code Security. Using its most advanced model, Claude Opus 4.6, the tool found over 500 high-severity vulnerabilities in production open-source codebases. These are bugs with security implications that had survived decades of expert review, continuous fuzzing, and multiple layers of automated scanning. The cybersecurity market reacted badly: roughly $15 billion …

AI applications are at tremendous risk. The complex chain of different components that AI applications depend on to truly push the boundaries of innovation is also what dramatically increases their attack surface, leaving them vulnerable to attackers.

Your company rolls out a new claims system powered by AI. It is fast, efficient, and customers love the experience. Then one day, a single malicious input slips through. The model is tricked into revealing sensitive records, or a workload spins out of control and consumes expensive GPU cycles. What began as a breakthrough quickly …

The rush to adopt generative AI has created an explosion of new security tools targeting different aspects of an increasingly complex landscape. Every week, a solution promises to stop prompt injection, prevent data leakage, or harden models against attack. For security leaders, it can feel overwhelming. Which solutions matter, how do they fit together, and …

From writing code to serving customers, companies worldwide are capitalizing on AI to enhance their applications and business processes. But this boom is poised to turn into a bust if security can’t keep pace with AI adoption. And make no mistake: LLMs and AI services, whether they are consumed by the application or hosted and …

Enterprises are rapidly adopting large language model applications, AI assistants, and model orchestration platforms at a pace that rivals the early days of cloud computing. With this acceleration comes both significant opportunity and risk. Yet AI security is lagging behind, struggling to match the speed and scale of adoption. One of the hottest topics right …

AI security is often discussed at the edge, through firewalls, proxies, SDKs, or prompt filtering. These approaches serve a purpose, but they overlook where many of the most serious risks actually take place, inside the container.

The line between human and machine-generated threats is starting to blur. Aqua Nautilus recently uncovered a malware campaign that hints at this unsettling shift. Koske, a sophisticated Linux threat, shows clear signs of AI-assisted development, likely with help from a large language model. With modular payloads, evasive rootkits, and delivery through weaponized image files, Koske …

Security teams are facing urgent questions as AI moves from experimentation to production. What models are running in our environment? Where are they deployed? Are they operating within policy and if not, can we stop them? Existing tools offer limited answers and they rarely provide governance without slowing developers down. To move forward, security leaders …

It started with a single LLM API call buried in a product prototype. By the time the team noticed, that call had turned into a full-blown feature. Customers were using it, business logic was being shaped by it, and the security team had not even seen it. That is the pattern we are seeing across …

Cloud adoption has transformed the enterprise IT landscape, but that transformation has not followed a clean or linear path. Instead of standardizing on a single platform, most organizations now operate across a patchwork of public clouds, private data centers, and legacy systems. Hybrid and multi cloud strategies have become the norm, not by design but …