FAQs

An AI Bill of Materials (AI-BOM) is a detailed inventory of all components used to build and deploy an AI system—such as models, datasets, libraries, agents, and APIs. It provides transparency, traceability, and accountability across the AI supply chain for security, compliance, and governance.

AI Compliance Monitoring ensures that AI systems align with internal policies, legal standards, and ethical guidelines. It involves tracking usage, logging decisions, detecting violations, and generating reports—giving organizations the visibility and control needed to meet frameworks like GDPR, NIST AI RMF, and the EU AI Act.

AI Discovery is the process of automatically identifying and cataloging AI-related assets—such as models, datasets, notebooks, and APIs—across development platforms. PointGuard AI Discovery helps organizations gain visibility, manage AI risk, and streamline governance by continuously monitoring their AI footprint across MLOps environments.

An AI Firewall is a runtime control layer that monitors and filters inputs and outputs between users and a large language model. It prevents prompt injection, jailbreaks, data leaks, and unsafe content generation—protecting applications that embed LLMs in production environments.

AI Governance is the framework of policies, processes, and controls used to ensure responsible and compliant use of AI. It addresses data integrity, model explainability, accountability, and regulatory alignment—ensuring that AI systems are secure, trustworthy, and aligned with organizational goals and ethical standards.

Jailbreaking is the process of manipulating an AI model—especially an LLM—to bypass safety filters or moderation policies. These attacks can force the model to generate content it’s designed to block, such as offensive language, harmful instructions, or private information.

Automated AI Red Teaming is the use of simulated attacks to test the resilience of AI models against adversarial threats. It proactively identifies vulnerabilities—such as jailbreaks, prompt injection, or toxic output—helping organizations strengthen model security before deployment.

AI Runtime Detection & Response monitors AI prompts and responses in real time to detect and prevent threats like data leaks, prompt injection, and misuse. It enforces policy inline and automates mitigation through integrations with enterprise ticketing and alerting systems.

AI Security Posture Management (AI-SPM) is the continuous monitoring and protection of AI systems to detect misconfigurations, vulnerabilities, and threats. It automates remediation, enforces compliance, and strengthens resilience across MLOps platforms—ensuring secure, trustworthy, and compliant AI development at every stage.

AI Supply Chain Risk refers to vulnerabilities introduced through third-party models, data, and software used to build and deploy AI systems. These risks include malicious components, license violations, or unvetted model behavior—posing security, legal, and operational threats to AI-powered applications.

AI Supply Chain Security protects the third-party code, models, data, and dependencies used in building and deploying AI systems. It ensures that vulnerabilities in open-source components or pre-trained models don’t compromise the security or integrity of AI-powered applications.

AI TRiSM (Artificial Intelligence Trust, Risk, and Security Management) is a comprehensive framework that ensures AI systems are trustworthy, secure, and compliant throughout their lifecycle. It addresses AI-specific risks like bias, data privacy, and model vulnerabilities to build transparent, reliable, and ethically governed AI solutions.

Application Security Posture Management (ASPM) is a unified approach to managing software vulnerabilities across the entire development lifecycle. It consolidates findings from multiple security tools, reduces noise, prioritizes threats based on business risk, and automates remediation workflows to improve visibility, response, and governance.

The EU AI Act is a first-of-its-kind regulation governing the development and use of artificial intelligence in the European Union. It classifies AI systems by risk level—banning some, regulating others—and mandates transparency, human oversight, and security for high-risk AI applications.

Generative AI Security refers to protecting systems built on models like GPT, Claude, and open-source LLMs from misuse, exploitation, or data compromise. It includes defenses against prompt injection, hallucinations, jailbreaks, data leakage, and supply chain vulnerabilities—ensuring safe use of AI in applications, agents, and APIs.

LLM Observability gives teams visibility into how large language models (LLMs) behave in production. It includes monitoring prompts, outputs, performance, risk signals, and user interactions—helping organizations detect failures, prevent abuse, and improve reliability of generative AI systems.

AI Model Drift Detection monitors changes in model behavior, data inputs, or output quality over time. It identifies when an AI model starts producing inaccurate, inconsistent, or biased results—helping organizations maintain model reliability, prevent performance degradation, and ensure alignment with intended use and regulatory expectations.

Model hallucination refers to an AI model generating false, misleading, or nonsensical information with high confidence. These fabricated outputs can misinform users or disrupt downstream processes—especially in LLM-powered applications that synthesize content, code, or responses without verifying factual accuracy.

Model Risk Management (MRM) is the process of identifying, assessing, and mitigating the risks associated with AI and machine learning models. It ensures that models are accurate, secure, and aligned with regulatory expectations—reducing the chance of failure, misuse, or unintended consequences.

The NIST AI Risk Management Framework (AI RMF) is a U.S. government-led guide that helps organizations develop and deploy trustworthy AI. It outlines practices for identifying, assessing, managing, and governing AI risks, with emphasis on security, privacy, fairness, transparency, and accountability.