Innovate With AI, Without Compromising Security
From securing AI-powered applications to protecting sensitive data and managing emerging threats, we help organizations build, deploy, and adopt AI with confidence.
THE CHALLENGE
AI Innovation Introduces New Security Risks
Artificial intelligence is transforming how businesses operate, automate processes, and deliver value. But as organizations integrate AI into applications, workflows, and critical business functions, they also introduce new security challenges.
Unlike traditional software, AI systems can be manipulated through inputs, exposed through insecure integrations, or granted access beyond their intended boundaries. These risks become more complex as organizations adopt autonomous AI agents and increasingly interconnected AI systems.
The challenge isn't just adopting AI, it's ensuring that innovation doesn't introduce risks your business isn't prepared to manage.
Emerging AI Threats
Prompt injection, model manipulation, and adversarial attacks introduce security risks that traditional testing may not fully address.
Expanding AI Attack Surface
AI-powered applications, external models, APIs, and autonomous agents create additional entry points and complex dependencies.
Uncontrolled AI Actions
Autonomous agents with access to sensitive information, business systems, or external tools can create unintended consequences when permissions and safeguards are inadequate.
HOW WE HELP
Secure AI Innovation, Build Business Confidence
At Scyverge, we help organizations identify and address security risks across AI-powered applications, models, and autonomous agents.
Our approach combines targeted security assessments, specialized AI application testing, and realistic adversarial simulations to uncover vulnerabilities that traditional security testing may overlook.
By understanding how AI systems can be manipulated, exploited, or misused, we provide actionable insights that help businesses strengthen safeguards and adopt AI with greater confidence.
Identify AI-Specific Risks
Uncover vulnerabilities across AI applications, models, integrations, and autonomous agents before they can be exploited.
Validate Security Controls
Evaluate how effectively your AI systems resist adversarial attacks, unauthorized actions, and attempts to bypass safeguards.
Turn Findings Into Action
Receive practical recommendations to address identified weaknesses, strengthen AI security controls, and reduce business exposure.
OUR CAPABILITIES
AI Security Across Every Stage of Adoption
From AI-powered applications to autonomous agents, we help organizations identify vulnerabilities, understand emerging threats, and strengthen the security of their AI systems.
AI Security Assessment
Evaluate AI systems, models, infrastructure, and integrations to identify security weaknesses and potential risks.
AI Agent Security
Assess autonomous AI agents and workflows for excessive permissions, unsafe tool execution, and unintended actions.
AI Application Security Testing
Identify vulnerabilities in AI-powered applications, APIs, and integrations, including prompt injection and sensitive data exposure.
AI Red Teaming
Simulate realistic adversarial attacks against AI systems to evaluate their resilience, safeguards, and security controls.
BUSINESS OUTCOMES
Secure AI, Enable Confident Innovation
AI security is about more than identifying technical vulnerabilities. It's about enabling organizations to adopt emerging technologies while maintaining control over security risks.
Scyverge helps businesses understand their AI exposure, strengthen critical safeguards, and make informed security decisions that support responsible innovation and long-term resilience.
Reduce AI Security Exposure
Identify and address weaknesses across AI applications, models, and autonomous agents before they lead to security incidents.
Make Informed AI Security Decisions
Understand AI-specific threats and prioritize security improvements based on potential business impact.
Strengthen AI System Resilience
Improve safeguards against adversarial attacks, unauthorized access, and unintended AI-driven actions.
Enable Confident AI Adoption
Support the deployment and expansion of AI-powered capabilities with greater visibility into security risks and controls.
OUR APPROACH
A Structured Approach to Securing AI
Securing AI requires an understanding of both traditional security vulnerabilities and the unique risks introduced by AI systems.
Our approach combines targeted assessments, adversarial testing, and risk-based analysis to help organizations identify weaknesses, evaluate existing safeguards, and strengthen the security of their AI-powered environments.
- 01
Understand & Scope
Understand your AI use cases, system architecture, integrations, and business objectives to define assessment priorities and testing boundaries.
- 02
Assess & Identify
Evaluate AI applications, models, agents, and supporting infrastructure to identify vulnerabilities, insecure configurations, and potential attack paths.
- 03
Simulate & Validate
Conduct controlled adversarial testing to evaluate prompt injection risks, safeguard bypasses, unauthorized actions, and other relevant AI-specific threats.
- 04
Analyze & Recommend
Assess validated findings based on exploitability and business impact, then provide clear remediation recommendations to strengthen AI security controls.
- 05
Remediate & Retest
Support remediation planning and, where included in the engagement, retest identified weaknesses to validate the effectiveness of implemented fixes.
FREQUENTLY ASKED QUESTIONS
Common Questions, Clear Answers
What is AI security?
AI security focuses on protecting AI systems, applications, models, and autonomous agents from vulnerabilities, adversarial attacks, unauthorized access, and misuse. It combines traditional cybersecurity practices with testing designed for AI-specific threats.
How is AI security testing different from traditional penetration testing?
Traditional penetration testing primarily evaluates vulnerabilities in applications, networks, and infrastructure. AI security testing also examines risks such as prompt injection, model manipulation, sensitive data disclosure, unsafe tool execution, and failures in AI safeguards.
What is AI red teaming?
AI red teaming involves simulating adversarial attacks against AI systems to identify weaknesses in their security controls and behavior. It helps organizations understand how AI applications and models respond to malicious inputs and misuse scenarios.
Why do autonomous AI agents require security testing?
AI agents can interact with external tools, access business systems, and perform actions with varying levels of autonomy. Security testing helps identify excessive permissions, unsafe tool usage, prompt injection vulnerabilities, and unintended actions that could affect business operations.
When should an organization conduct an AI security assessment?
AI security assessments are particularly valuable before production deployment, after significant changes to models or integrations, and when introducing autonomous agents or sensitive business workflows. Testing frequency should reflect the organization's risk exposure and pace of AI development.
What will we receive after an AI security engagement?
Depending on the agreed scope, deliverables typically include documented findings, supporting evidence, risk prioritization, and actionable remediation recommendations. Retesting may also be included to validate implemented fixes.
TAKE THE NEXT STEP
Build With AI, Move Forward Securely
Whether you're deploying AI-powered applications, introducing autonomous agents, or evaluating existing AI systems, Scyverge helps you understand security risks and strengthen safeguards.