AI application security is the process of making AI-powered applications safe by securing their underlying components against vulnerabilities. Our solution uses policy-based controls, adaptive frameworks, and real-time threat detection to ensure resilient, compliant, and secure AI deployments. With industry-aligned protections, we tackle complex security and operational risks, ensuring your AI applications remain strong and dependable across environments.
Unlike traditional security, which relies on manual checks and set rules, AI AppSec is quick, adaptive, and always learning. It spots threats faster, adjusts to new risks on its own, and keeps apps secure with less hassle—making security smarter and simpler.
Traditional applications are deterministic, producing consistent outputs from the same inputs, making their vulnerabilities predictable and manageable with standard security measures. In contrast, AI applications generate variable outputs, leading to unique vulnerabilities that require advanced mitigation techniques beyond traditional security.
AI applications evolve through continuous learning from feedback or data, leading to new vulnerabilities post-deployment. This adaptive nature requires ongoing monitoring and an agile security approach, unlike traditional software, which remains static unless explicitly modified.
Vulnerability testing for AI applications requires specialized techniques to address unique risks like adversarial attacks and data poisoning. Unlike traditional applications that use static analysis and penetration testing, AI models need tailored testing to uncover vulnerabilities in their data dependencies and algorithms.
Traditional application security relies on singular defenses like firewalls and antivirus software, leaving networks vulnerable to complex, multi-vector attacks. AI application security requires multi-layered defenses that integrate adaptive, model-specific protections to address diverse vulnerabilities across data, algorithms, and real-time learning processes.
Traditional applications use Web Application Firewalls (WAF) to filter HTTP traffic and block attacks like SQL injection and XSS. In contrast, AI applications employ AI Firewalls specifically designed to address unique security challenges, such as PII leakage, prompt injection, and DoS attacks.
Global security standards are essential for fortifying application defenses against sophisticated and evolving cyber threats. Standards like ISO/IEC 27001, NIST, and OWASP provide rigorous frameworks that guide organizations in protecting data integrity, securing open-source components, and ensuring compliance across digital ecosystems.
ISO/IEC 27001 is a globally recognized standard for information security management systems (ISMS), providing a systematic approach to managing sensitive information. It emphasizes the importance of application security throughout the software development lifecycle (SDLC) and introduces specific controls aimed at mitigating risks associated with application vulnerabilities.
Risk-based ISMS framework
Continuous improvement via audits
Vulnerability Management
The OWASP Top 10 highlights the most critical web application vulnerabilities, including injection flaws, security misconfigurations, and broken authentication. By following these guidelines, developers and organizations can significantly improve application security, adopting secure coding standards and implementing best practices that reduce exposure to common, high-impact risks.
Key risks: XSS, SQL Injection, CSRF
Secure SDLC integration
Actionable guidelines for developers
The NIST Cybersecurity Framework provides a risk-based approach to managing cybersecurity through a structured set of core functions: Identify, Protect, Detect, Respond, and Recover. It serves as a universal model for enhancing application resilience against advanced persistent threats. It is designed to be applicable across various industries and organizational sizes.
Layered defense approach
Adaptive security controls
Continuous threat lifecycle management
SOC 2 is a compliance framework that focuses on how organizations manage customer data based on five Trust Services Criteria (TSC): Security, Availability, Processing Integrity, Confidentiality, and Privacy. It is particularly relevant for service organizations, especially those in the technology sector, to demonstrate their commitment to protecting sensitive information.
Access controls and monitoring
Incident response protocols
Transparent SOC 2 reporting
GDPR mandates robust data protection measures to safeguard personal data and uphold user rights within the European Union. It necessitates data encryption, access controls, and privacy impact assessments to maintain compliance and mitigate security risks.
User rights: DSARs, data portability
Data Protection Impact Assessments (DPIAs)
Mandatory breach notifications
The Center for Internet Security (CIS) provides critical guidelines for application security through its CIS Control 16: Application Software Security. This control emphasizes managing the security lifecycle of software to prevent, detect, and remediate vulnerabilities effectively.
Secure Development Process
Threat Modeling
Access Control Measures
CyberGen provides advanced security solutions designed to address the unique vulnerabilities in AI applications. With policy-based frameworks, real-time threat detection, and adaptive guardrails, our approach reinforces defenses across data, model integrity, and compliance.
CyberGen employs adaptive Guardrails to wrap AI applications within a secure, controlled environment, safeguarding the entire development and operational lifecycle. By implementing real-time monitoring and controls, our guardrails ensure that AI models operate within defined, safe parameters, reducing risks and enhancing the reliability of model performance.
CyberGen’s AI Guardrails secure AI applications by implementing rigorous checks on both input and output to prevent risks such as prompt injection, jailbreaking, Base64 encoding attacks, and data poisoning. These guardrails also address biasness, content toxicity, and personal information disclosure, ensuring AI models operate securely, ethically, and within compliance boundaries.
We implement Rego policies within our policy-driven security framework to enforce role-specific policies and access limitations, providing precise control over application access, compliance, and operational safeguards. This adaptive, rulebased approach enables swift threat response and continuous compliance, delivering robust security for AI deployments in dynamic and complex environments.
At CyberGen, we prioritize securing LLM applications by adhering to the critical security measures outlined in the OWASP Top 10 for LLMs. Our approach includes advanced security frameworks that address high-risk vulnerabilities, ensuring the confidentiality, integrity, and availability of AI systems. Through robust risk mitigation, we safeguard against complex AI-specific threats such as data manipulation, unauthorized access, and malicious inputs that could compromise model behavior.
Data Poisoning Checks
Output Filtering & Toxicity Detection
Sensitive Information Disclosure
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