Organizations are deploying AI faster than they're securing it. Kammerdiener Technologies builds the infrastructure, pipelines, and security controls that let your AI initiatives scale without introducing critical risk.
Only 1% of organizations believe their AI adoption has reached maturity — yet AI is already generating a quarter of new code and is on track to reshape how software is built. The gap between what organizations are deploying and how well they understand its security implications is widening every month.
AI code builds can only go so far without proper infrastructure beneath them. The complexity ceiling is real: AI-generated code that isn't backed by a secure, well-structured pipeline creates technical debt, security exposure, and operational fragility at speed.
Executives want to adopt AI to attract and retain top talent. But deploying AI tools as individual utilities — rather than as governed organizational infrastructure — creates new risk vectors without the governance to match.
AI models and pipelines are susceptible to adversarial inputs that corrupt outputs or leak sensitive data. Without purpose-built controls, these aren't hypothetical risks — they're active attack surfaces.
Agentic AI systems take actions autonomously — calling APIs, accessing data, executing code. Without proper identity, access controls, and audit trails, a single compromised agent can traverse your entire infrastructure.
AI models depend on the integrity of their training data and the confidentiality of their inputs and outputs. Treating AI models as regular software artifacts — with proper version control, access control, and governance — is not yet standard practice.
AI-generated code hits a ceiling when the underlying infrastructure isn't ready for it. Without robust CI/CD integration, automated security scanning, and proper orchestration, AI development creates faster-moving risk, not faster-moving product.
Four interconnected service lines that address the full lifecycle of AI security — from the infrastructure layer to runtime governance.
We build the robust foundation that AI development requires. Deep expertise in CI/CD integration, automation, Chainguard secure images, and Bitnami ensures your AI projects have a secure, scalable pipeline from code to production.
Breaking through the complexity ceiling — providing the reliable platform that lets AI models deploy and iterate seamlessly.
Agentic AI systems require proper identity management, least-privilege access policies, and audit trails for every autonomous action. We design the governance layer that lets AI agents operate safely — with visibility, accountability, and controllable blast radius.
Emerging capability serving the next wave of enterprise AI deployments.
Protecting AI models, applications, and training data from prompt injection, data poisoning, and unauthorized access. We ensure confidentiality, integrity, and safety throughout the AI lifecycle — guided by SAIF and responsible AI principles.
Covers model security, input/output validation, and runtime protection from development to edge.
Treating AI models as software artifacts — with version control, vulnerability scanning, and access governance. Using the JFrog Platform's ML and AI Catalog capabilities, we integrate AI model management into your existing DevSecOps pipeline.
Part of our JFrog Authorized Partnership — AI model security from training to production.
Zero Trust — the principle of "never trust, always verify" — applies as much to AI agents and pipelines as it does to traditional network infrastructure. In an agentic AI environment where autonomous systems are calling APIs, accessing data stores, and making decisions, the default assumption must be that no actor is trusted without verification.
We implement Zero Trust frameworks tailored to AI environments: identity-based access control for agents and models, micro-segmentation of AI workloads, continuous monitoring of AI traffic, and cryptographic audit trails for every AI decision and action.
Every AI agent, model, and pipeline component must have a verified identity before accessing any resource.
AI agents get the minimum permissions required — and only for the minimum time required. Short-lived credentials, not long-lived keys in plain text.
Design AI systems assuming compromise is possible. Limit blast radius, segment workloads, and maintain comprehensive audit trails.
Access decisions are made in real time — not once at session start. AI agent behavior is continuously monitored and validated against policy.
AI code hits a complexity ceiling when the underlying infrastructure isn't up to the task. We build what's underneath — so your AI projects can ship, scale, and iterate without friction or exposure.
Using Chainguard hardened container images, we eliminate known CVEs at the base layer — so AI workloads run on a provably secure foundation, not on bloated images with hundreds of unpatched vulnerabilities.
CI/CD pipelines built for AI — incorporating security scanning, model validation, dependency checks, and deployment automation. AI development moves fast; your pipeline should move at the same speed, safely.
Orchestrate AI inference and training workloads at scale using Kubernetes and Helm Charts. We manage resource allocation, autoscaling, and workload isolation so your AI systems have reliable, cost-optimized compute.
Managing AI models the same way you manage software packages — with version control, vulnerability scanning, access policies, and signed distribution. JFrog ML and AI Catalog bring your AI supply chain under control.
AI inference costs can scale unpredictably. We build cost prediction, monitoring, and alerting into AI infrastructure — giving you visibility into spend before it becomes a surprise, with right-sizing built in from the start.
Emerging AI compliance frameworks — including NIST AI RMF and ISO 42001 — require documented governance of AI systems. We integrate AI compliance into your existing security and audit posture.
Dramatic security improvements don't always require dramatic overhauls. The right change in the right place can multiply your security posture by an order of magnitude.
In one recent engagement, a client was generating a service account key in GCP, dropping it into a plain-text config map, and mounting it into the application. A very common pattern — and a significant exposure. The key was long-lived, broadly accessible, and stored in a human-readable format.
The fix: generate the credential at runtime instead. Short-lived, automatically rotated, scoped to only the project that needs it. Same functionality. Dramatically reduced attack surface. One architectural decision.
That's the 10X security shift. Not a platform overhaul — a targeted change that eliminates an entire class of credential-exposure risk without disrupting the application.
Deploying AI into payment platforms, data pipelines, and client-facing systems — where a security incident is also a compliance failure and a reputational event.
Government-grade AI deployments require classified-level rigor on model integrity, data classification, and supply chain governance from day one.
Large organizations adopting AI as an organizational tool — not just individual utilities — need the governance layer that makes enterprise AI adoption safe and sustainable.
Companies do not have the resources to focus on DevSecOps — let alone AI security — so they outsource to a team that does. That's exactly what we're here for.
Talk to our team about your AI environment, your current exposure, and what it would take to harden it. We'll give you a straight answer.