An AI Security Platform (AISP) is a centralized system designed to protect both third-party and custom-built AI applications. It combines AI usage control and AI application cybersecurity to monitor, enforce policies, prevent data leaks, and secure AI systems across the organization.

‍Why it matters: AISPs reduce AI-native security risks, provide unified visibility, and simplify governance for enterprise AI adoption.

AI security threats are risks and vulnerabilities that target or arise from AI systems, potentially compromising their integrity, confidentiality, or availability. Such threats can include adversarial attacks, data poisoning, model inversion, unauthorized inference, and misuse of AI for malicious purposes.

Read more about these risks and how to mitigate them in the Knowledge Center.

AI trust is the confidence that AI systems behave reliably, securely, and in alignment with intended goals and constraints. It involves assurance that outputs are explainable, fair, accountable, and consistent over time.

‍Why it matters: Without trust in AI decisions, organizations cannot safely scale adoption or meet stakeholder, customer, and regulatory expectations.

AI Usage Control (AIUC) enforces organizational policies for safe consumption of AI services, particularly third-party tools. It monitors usage, prevents sensitive data leaks, and controls what topics or outputs employees can generate with AI.

‍Why it matters: AIUC ensures AI is used responsibly, mitigates insider and operational risks, and maintains compliance with governance policies.

Attribute Based Access Control (ABAC) is a security approach that uses attributes (such as title, location, team, etc) to determine access to a resource. A system administrator would be the one to set approved characteristics to determine access. 

Learn more here.

Authorization, or AuthZ, is a critical enabler of most systems, be that workforce environments or consumer applications. Based on a set of policies, it determines what actions users are permitted to perform and what resources they can access. Modern approaches use authorization as a key driver of personalized experiences, ensuring efficient and secure access tailored to each user’s role and context.

Learn more here.

AuthZen (Authorization Enhancement) is a standard by the OpenID Foundation that defines an interoperable protocol for fine-grained authorization. It enables Policy Enforcement Points (PEPs) and Policy Decision Points (PDPs) to work together using rich contextual data to make precise access decisions.

‍Why it matters: AuthZen helps organizations enforce precise, policy-driven access decisions, improving control, compliance, and interoperability.

B2B data sharing involves securely sharing or accessing data from one entity to another for business purposes. For example to enable collaboration, improve services or simply create mutual value. This often involves sharing customer insights, supply chain data or analytics, while ensuring privacy, security and compliance with regulations.

Why it matters: Secure, well governed data sharing drives collaboration, innovation, and value creation without exposing sensitive information.

Learn more about B2B data sharing here.

Connected data refers to data stored on a graph data model, which enables relationships between data points. Graph has a unique ability to understand dynamic and complex relationships and manages data in a more natural, intuitive way, giving context to otherwise meaningless information. Connected data is a powerful force, offering greater flexibility, insight and speed for data driven projects.

Why it matters: Connected data enables richer analysis, more precise authorization, and faster response to business or security needs.

Connected data models involve networks of data points or nodes linked through relationships. Knowledge graphs are a popular way to do this, making connections between disparate sources to provide specific insights. They aim to intuitively represent the interconnected world. The real world is flexible, messy and constantly changing. Our relationships and connections are dynamic and are at times incredibly complex and layered, and knowledge graphs are designed to reflect this complexity.

Context-aware enforcement is a type of dynamic enforcement that applies access and security policies specifically based on real-time contextual information, such as user identity, device status, location, and behavior. By evaluating the circumstances surrounding each request, it ensures that access or actions are only allowed when appropriate.

Why it matters: By using contextual intelligence, organizations can prevent unauthorized actions while enabling legitimate system use, enhancing security without disrupting productivity.

Context-aware security is the practice of using situational information to enhance security decisions in real time. It takes into account factors like user location, device type, time of access, and network conditions to dynamically adjust access controls and security measures. This approach enables more adaptive and precise protection - reducing the risk of threats while still allowing legitimate users to access what they need.

‍Why it matters: Adaptive, context-based security reduces threats more precisely than static rules, strengthening protection without hindering productivity.