FAQs

The OWASP Top 10 for LLMs is a widely adopted list of the most critical security risks affecting large language model (LLM) applications. It includes threats like prompt injection, data leakage, and insecure plugins—helping developers and security teams prioritize defenses across the generative AI lifecycle.

PointGuard AI is the only platform that secures both AI systems and traditional application stacks. It uniquely integrates AI Security and ASPM, offering full lifecycle protection—from source code and open-source packages to models, agents, and connected apps—with automated remediation and policy enforcement.

PointGuard AI was founded by CEO Pravin Kothari, Chief Product & Security Officer Mali Gorantla, and Chief Technology Officer Warlu Kothapalli. Together, they bring decades of leadership across cloud security, product innovation, and AI-driven engineering.

PointGuard AI was founded as AppSOC in 2022. It rebranded in 2025 to reflect its expanded focus on AI Security and Governance, building on its foundation in Application Security Posture Management (ASPM).

Prompt Injection is a type of adversarial attack where users manipulate inputs to trick an AI model into ignoring its intended instructions. These attacks can bypass safety rules, hijack model behavior, or extract sensitive information—posing serious security risks in LLM-powered applications and agents.

Prompt Logging records all user inputs (prompts) and model-generated outputs within an AI system. It enables auditing, debugging, and compliance by maintaining a traceable record of interactions—helping teams detect misuse, resolve incidents, and verify that AI behavior aligns with policies and expectations.

Risk-Based Vulnerability Management (RBVM) is the practice of prioritizing vulnerabilities based on business impact, exploitability, and context—not just severity scores. It helps organizations reduce alert fatigue, focus remediation efforts, and improve security posture by addressing the risks that matter most.

AI Runtime Monitoring continuously observes prompts, model outputs, and interactions as AI systems run in production. It detects threats like prompt injection, data leakage, and jailbreaks in real time—providing visibility and control over how AI is used across live applications and environments.

Shadow AI refers to artificial intelligence tools, models, or agents adopted by employees or teams without approval from security, IT, or governance groups. Like shadow IT, it introduces unmanaged risks—including data leakage, policy violations, and exposure to unvetted models or APIs—outside of centralized visibility and control.

A shadow model is an AI model deployed or developed without visibility or approval from the security team. Like shadow IT, shadow models bypass governance, introducing unmanaged risk—from unvetted datasets to insecure endpoints or unclear licensing—and often go undetected in modern development environments.