Protect AI systems, models, and pipelines from adversarial attacks, data leakage, and model risk — before they're exploited.
AI introduces a category of vulnerabilities that traditional security controls don't address. Prompt injection can hijack an LLM's behavior. Training data can be poisoned to alter model outputs. Sensitive business data passes through third-party APIs that retain and train on it. These aren't hypothetical — they're active threat vectors being exploited today.
Model inversion attacks can extract training data from a deployed model. Adversarial inputs can fool classifiers into making dangerous decisions. AI systems that lack output monitoring can leak confidential information at scale, often without triggering any existing security alert.
Standard application security testing doesn't cover these vectors. You need AI-specific threat models, adversarial testing, and security controls designed for the unique way AI systems process and generate information.
From adversarial testing to architecture review — we secure your AI stack at every layer.
A structured evaluation of your AI stack for vulnerabilities across model design, data pipeline, infrastructure, and API integration — with a prioritized remediation roadmap.
Red-team your LLM applications for prompt injection, jailbreaking, and guardrail bypass — using structured adversarial testing based on OWASP LLM Top 10 and MITRE ATLAS.
Ensure sensitive data isn't leaking through prompts, API logs, or model outputs. Design data classification and handling rules specific to AI workflows.
Assess, document, and mitigate risks from AI model behavior — including reliability failures, bias, output manipulation, and regulatory exposure.
Due diligence on AI APIs, LLM providers, and AI SaaS tools — covering data retention, training data practices, security certifications, and contractual protections.
Design secure pipelines, access controls, output monitoring, and anomaly detection for AI systems — built to detect and contain threats before they cause damage.
If your product includes an LLM-powered feature — chatbots, AI agents, copilots — you have AI-specific attack surface that needs to be tested and hardened.
If AI tools touch customer data, financial records, or proprietary business information, you need to know where that data goes — and what risks exist.
You understand traditional security but need AI-specific threat models, testing methodologies, and controls to keep pace with how your organization is using AI.
Prompt injection is when malicious input manipulates an AI model into ignoring its instructions, leaking data, or taking unintended actions. It's one of the most exploited AI vulnerabilities in production systems today — and one that standard security tools can't detect.
We apply OWASP LLM Top 10, NIST AI Risk Management Framework, and MITRE ATLAS as our primary frameworks — tailored to your specific AI stack, threat model, and regulatory context.
Yes. We conduct structured red-teaming and security assessments on deployed AI applications, APIs, and data pipelines — identifying vulnerabilities and providing clear, actionable remediation guidance.
No. If your organization uses third-party AI tools — ChatGPT, Microsoft Copilot, Claude, Gemini, or others — with business data, you have AI security risks worth managing. You don't need to build AI to be exposed.
AI introduces fundamentally new attack vectors — prompt injection, model behavior manipulation, training data risks, output leakage — that traditional AppSec tools and methodologies don't cover. Both types of testing are needed for AI-powered systems.
Don't wait for a breach to discover your AI has attack surface. Let's assess it now — before it becomes a problem.
Book a Free Consultation