Cequence Security Features

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.

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.

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).

Enforces strict request and token quotas per user, tenant, or application to prevent system overloads and manage API budget limits.

Caches responses for identical or semantically similar prompts to significantly reduce API latency and eliminate redundant token costs.

Routes API requests to various LLM providers via a single unified API and automatically fails over to backup models if downtime occurs.

Intercepts traffic to detect and mask Personally Identifiable Information (PII) or block malicious prompt injections before the request reaches the external model.

Provides granular dashboards to monitor token consumption and calculate exact monetary costs broken down by application, team, or specific model.

Securely vaults and manages external LLM API keys within the gateway so developers do not need to hardcode credentials into their applications.

Provides visibility into bot activity, attack trends, and traffic composition through dashboards and reporting

Distinguishes approved bots such as search engine crawlers and partner integrations from malicious automation

Identifies and classifies malicious automated traffic across web applications, APIs, or digital services

Uses machine learning or AI models to identify sophisticated or evolving bot behavior patterns

Identifies, classifies, and manages traffic generated by AI agents, autonomous agents, or LLM-based systems

Dynamically adjusts detection and mitigation decisions based on AI-driven risk analysis

Automatically blocks, challenges, rate limits, or otherwise mitigates malicious bot traffic

Protects APIs from automated attacks, scraping, excessive requests, and unauthorized access attempts

Detects and prevents credential stuffing, account takeover, and automated login abuse

Monitor the IP addresses of the incoming traffic

Automatically identify and flag unusual behaviors and malicious software

Identify and prevent suspicious activity

A chronological record of actions or occurrences within a network, software, or process

Reduce or block bot traffic

Discover patch statuses and vulnerabilities

Receive notification of issues as soon as they occur

Information to prevent, understand and identify cyber threats

Identify bot traffic

Assembly of graphs and charts for visualizing and tracking statistics/metrics

Software program that continuously adjusts its behavior based on observed data

Use AI to generate content in the form of text, images, videos, etc.

A virtual assistant that uses AI to pursue goals and complete tasks on behalf of users

Track and analyse user behavior within a system or network

Provide specialized, emerging, or cross-functional capabilities that enhance processes, workflows, or decision-making

Have features and use cases that do not fit into existing AI software categories

Address unique or niche requirements not covered by mainstream software solutions