AppTrana Features

Caching of static or dynamic content at distributed PoPs for faster load times.

Reporting and analytics that report on activity and program effectiveness.

Identifies and alerts administrators of threats, issues, incidents and requests related to website security.

Tracking recipient tagging of domains for blacklists and whitelists.

Analyzes your existing website and network to outline access points that can be easily compromised.

Analyzes data associated with web traffic and site performance to provide vulnerability insights and best practices.

Protects websites and web applications from a variety of attacks and malware threats.

Protects against distributed denial of service (DDoS) attacks.

Discovers website and web application malware.

Facilitates the remediation of website and web application malware.

Provides required reports to manage business. Provides adequate logging to troubleshoot and support auditing.

Track issues and manage resolutions.

Detects anomalies in functionality, user accessibility, traffic flows, and tampering.

Gives user configurable rules to control requests, protocols and policies.

Limits access to suspicious visitors and monitors for traffic spikes to prevent overloads like DDoS attacks.

Lets users provision networks, deliver content, balance loads, and manage traffic.

API / Integrations

Provides the ability to extend the platform to include additional features and functionalities

Control and filter bot traffic.

Detects new and undocumented assets as they enter a network and add them to asset inventory.

Detects anomalies in functionality, user accessibility, traffic flows, and tampering.

Provides results of the simulation and reveals potential security gaps or vulnerabilitites.

Tools to track and implement required security policy changes.

Monitors data quality and send alerts based on violations or misuse.

Allows users to set customizable API verification settings to improve security requirements.

Runs pre-scripted security tests without requiring manual work.

Abilities to set standards for network, application, and API security risk management.

Constantly monitors activity related to user behavior and compares activity to benchmarked patterns.

Monitors for and rids systems of bots suspected of committing fraud or abusing applications.

Condenses long documents or text into a brief summary.

Condenses long documents or text into a brief summary.

Provides specific capability to defend AI assets from adversarial attacks (including prompt injection, data poisoning, model inversion or extraction) without requiring retraining of the underlying model.

Enables automatic inspection of model inputs (prompts) and/or outputs (responses) to prevent unsafe, sensitive or manipulated content.

Monitors the integrity of model weights, dependencies or metadata (for example via SBOM/AIBOM) to detect tampering, drift or unauthorised modification.

Verifies the ability to enforce who or what (users, agents, systems) may access a model or LLM asset.

Detects unusual or harmful runtime behaviour of AI models, agents or workflows (such as unexpected output patterns, excessive permissions use, or unknown agents).

Provides a persistent, searchable audit log of AI‑asset inputs, outputs and interactions (including who/what invoked the model, when, and with what data) for forensics and compliance.

Ensures that the AI‑security platform supports scaling of AI‑asset protection (models, agents, multi‑cloud deployments) and applies governance/compliance frameworks as AI usage grows.

Enables integration of the AI‑security solution with traditional security stacks (SIEM, SOAR, cloud security, application security, identity/access management) to unify visibility and response.

Offers visibility into unmanaged or unauthorized AI/agent use (“shadow AI”) across the organisation and enforces control over such usage (e.g., agent creation, LLM‑based services).

Supports codified, machine‑enforceable security policies targeting AI models/agents (for example, blocking certain categories of prompts, enforcing least‑privilege for model use, enforcing “no external data” rules).