Traditional WAFs primarily rely on static rules, signatures, and pattern matching to identify malicious requests. These systems work well against known attack payloads but often require constant tuning, maintenance, and rule updates.
GladiosWAF takes a different approach.
Instead of relying purely on predefined signatures, GladiosWAF uses a machine learning-based Intent Analysis engine that evaluates the structure, behavior, and intent of HTTP requests in real time.
Traditional WAFs inspect patterns. GladiosWAF interprets behavior.
Yes.
GladiosWAF is specifically designed to analyze behavioral and structural characteristics of requests rather than relying only on known attack signatures.
AI-generated attacks often mutate payloads, obfuscate patterns, distribute malicious logic, or avoid known signatures entirely.
Because GladiosWAF focuses on intent analysis instead of exact payload matching, it can detect many AI-generated and heavily obfuscated attack attempts that traditional signature-based systems may struggle with.
Yes — this is one of the key advantages of machine learning-based protection.
Traditional rule-based WAFs typically require known signatures, predefined patterns, or manually written rules.
GladiosWAF instead evaluates request structure, behavioral anomalies, payload composition, and malicious intent indicators.
This allows the system to identify suspicious behavior even when the exact payload has never previously existed.
Behavioral request analysis means evaluating how a request behaves structurally and contextually, rather than simply searching for suspicious keywords.
Instead of asking: “Does this payload contain a known attack string?”
GladiosWAF asks: “Does this request structurally behave like malicious activity?”
For example, fragmented injection attempts, encoded payloads, suspicious parameter relationships, abnormal request structures, and manipulation patterns can indicate malicious intent even when no obvious signature exists.
This allows GladiosWAF to detect attacks that appear syntactically clean but behaviorally malicious.
Traditional WAFs commonly rely on regex rules, keyword matching, static signatures, and manually configured policies.
This can cause legitimate requests to be blocked simply because they contain suspicious-looking text.
For example, SQL keywords inside blog posts, JSON payloads containing special characters, encoded user input, or developer API requests may trigger false positives even when they are legitimate.
GladiosWAF reduces false positives by analyzing context, intent, request structure, and behavioral patterns instead of relying solely on keyword detection.
Intent Analysis is GladiosWAF’s machine learning process for determining whether a request appears malicious or non-malicious.
When an HTTP request is received, GladiosWAF analyzes URL paths, query parameters, headers, body content, and structural relationships to determine the probable intent behind the request.
Unlike traditional WAFs that depend on thousands of static rules, GladiosWAF uses AI models trained on malicious and legitimate traffic patterns to make real-time security decisions in milliseconds.
Yes.
Attackers commonly attempt to bypass traditional WAFs using encoding, payload splitting, nested JSON structures, character substitution, fragmented injections, and polymorphic payloads.
Because GladiosWAF evaluates the behavioral structure and intent of requests, it can detect many forms of obfuscation even when the payload no longer matches known signatures.
Yes.
GladiosWAF is designed to protect REST APIs, JSON APIs, backend services, mobile application APIs, and traditional web applications.
Every HTTP request can be analyzed in real time regardless of whether it originates from browsers, mobile apps, backend services, or third-party integrations.
Yes.
GladiosWAF supports analysis of structured payloads including JSON, nested JSON objects, query parameters, headers, and request bodies.
This is especially important for modern API environments where attacks are often embedded deeply inside structured request data.
Yes.
GraphQL introduces unique attack surfaces including deeply nested queries, excessive query complexity, introspection abuse, and parameter manipulation.
GladiosWAF can analyze GraphQL request structures and behavioral characteristics to help detect malicious usage patterns and suspicious payload behavior.
GladiosWAF is designed for low-latency real-time inference.
Typical Intent Analysis latency is usually within the tens of milliseconds range, depending on deployment architecture, payload size, hardware, and network distance.
The system is optimized to provide AI-driven protection without significantly impacting application responsiveness.
GladiosWAF supports multiple operational modes depending on your security requirements.
This gives organizations flexibility depending on their risk tolerance and uptime requirements.
Shadow Mode allows GladiosWAF to analyze requests, generate security decisions, and log malicious activity without actually blocking traffic.
This is useful for testing deployments, evaluating detection accuracy, reducing deployment risk, and tuning operational visibility before enabling active blocking.
Yes.
GladiosWAF supports on-premise deployments, private infrastructure, isolated environments, and air-gapped deployments.
This allows organizations to maintain data sovereignty, regulatory compliance, and internal network isolation without requiring external cloud connectivity.
Yes.
GladiosWAF can run on compact hardware deployments including mini PCs, edge gateways, private appliances, and local inference nodes.
This enables AI-powered protection closer to the application edge with lower latency and improved data control.
Yes.
GladiosWAF can complement existing security infrastructure including Cloudflare WAF, ModSecurity, NGINX, API gateways, reverse proxies, and enterprise firewalls.
Many organizations deploy GladiosWAF as an additional AI-driven decision layer alongside traditional rule-based protection.
AI-generated attacks can mutate payloads dynamically, obfuscate malicious logic, distribute attacks across parameters, and avoid known signatures.
Traditional WAFs often depend on static pattern recognition, which makes highly adaptive payloads more difficult to detect.
GladiosWAF focuses on request intent, behavioral structure, contextual relationships, and anomaly characteristics to improve detection of modern adaptive attacks.
A request can appear syntactically normal while still behaving maliciously.
Keyword matching only checks whether specific strings exist.
Intent Analysis evaluates how the request is structured, how parameters interact, how payloads behave, and whether the overall request resembles malicious activity.
This allows GladiosWAF to detect sophisticated attacks that bypass traditional signature detection methods.
