Aqua Secure AI Wins AI Cybersecurity Solution of the Year

Aqua Cloud Native Blog \ Tags: AI Security

Aqua Secure AI Wins AI Cybersecurity Solution of the Year

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.

Secure AI Infrastructure On-Premises from Day One

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 …

Navigating the OWASP LLM and GenAI Security Landscape

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 …

Why AI-SPM Alone is Not Enough to Secure AI Workloads

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 …

MCP to Agentic AI: Shaping AI Security for What’s Next

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 Container Security Begins Inside the Workload

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.

SECURITY RESEARCH

AI-Generated Malware in Panda Image Hides Persistent Linux Threat

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 …

Operationalizing AI Security: Protecting Workloads Where AI Runs

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 …

Securing LLM Apps with Aqua: Beyond the OWASP Checklist

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 …

Unify to Fortify: Security for AI, Containers, and Every Cloud

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 …

Mainframes Are the New AI Infrastructure. Protect it with Secure AI

If your AI workloads run in containers, then securing those containers is the first and most important step in protecting your AI. And as enterprises begin to deploy containerized AI workloads on Red Hat OpenShift for mainframe environments, that priority becomes even more urgent.

From Prompt to Production: Runtime Protection for AI Workloads

The moment your app calls an LLM, it stops being just containerized software, and becomes an entirely new attack surface. Artificial Intelligence is transforming how applications behave and how they need to be secured. As generative AI and large language models (LLMs) become embedded into everything from customer support bots to internal data tools, security …

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Aqua Security is the pioneer in securing containerized cloud native applications from development to production. Aqua's full lifecycle solution prevents attacks by enforcing pre-deployment hygiene and mitigates attacks in real time in production, reducing mean time to repair and overall business risk. The Aqua Platform, a Cloud Native Application Protection Platform (CNAPP), integrates security from Code to Cloud, combining the power of agent and agentless technology into a single solution. With enterprise scale that doesn’t slow development pipelines, Aqua secures your future in the cloud. Founded in 2015, Aqua is headquartered in Boston, MA and Ramat Gan, IL protecting over 500 of the world’s largest enterprises.

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