AppSec, Cloud & GenAI Security

Deep technical execution embedded directly into your R&D. We secure your Code, Kubernetes infrastructure, and AI pipelines without slowing down innovation.

Secure SDLC

Data security

Cloud security

Secure AI adoption

Embedded Partnership. For modern product companies, security cannot be a bottleneck. Up Security integrates directly into your R&D and DevOps processes, acting as your internal AppSec and Cloud Security engineers. We bridge the gap between security requirements and engineering reality, ensuring that your product is secure by design from the first line of code to production deployment.

Cloud-Native & Kubernetes Mastery. We live and breathe modern infrastructure. Our team specializes in hardening Kubernetes clusters, Container environments, and Multi-Cloud architectures (AWS/Azure/GCP). We move beyond basic configuration checks to implement robust “Infrastructure as Code” (IaC) scanning and real-time runtime protection, ensuring your foundation is solid and resilient against advanced attacks.

GenAI & LLM Security. As you adopt AI, new risks emerge. We provide specialized protection for your GenAI initiatives, from preventing Prompt Injection and Data Leakage in public LLMs to securing the MLOps pipelines of your internal models. We help you define usage policies and implement technical controls (like LLM Firewalls) that allow your team to leverage AI safely.

Automation & Culture. Manual security reviews don’t scale. We automate security testing (SAST/DAST/SCA) directly into your CI/CD pipelines, catching vulnerabilities before they merge. Beyond tools, we invest in your people. Through tailored hands-on training (using platforms like SecureFlag), we transform your developers into security champions, reducing the number of bugs introduced in the first place.

Key tasks

Secure SDLC Implementation: Embed automated security gates (SAST, DAST, SCA) into CI/CD pipelines (GitHub/GitLab/Jenkins).
Kubernetes Hardening: Secure cluster configurations, manage network policies, and implement runtime defense for containerized workloads.
GenAI Risk Management: Assess and mitigate risks associated with LLM adoption, including prompt injection, model theft, and data privacy.
Secure Architecture Reviews: Partner with R&D architects during the design phase to identify threats (Threat Modeling) before coding begins.
Secrets Management: Implement robust policies and tools to detect and prevent hardcoded credentials and API keys in code.
Developer Training: Deliver role-specific, hands-on secure coding workshops to build a security-first engineering culture.
Cloud Security Posture (CSPM): Continuous monitoring and remediation of cloud misconfigurations and compliance drifts.

Let’s talk

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