Applications now underpin how organizations deliver services, interact with customers, and run core operations. Every transaction, support exchange, and internal workflow moves through a web app, mobile interface, or API, which has made these software surfaces frequent targets for attackers.
As codebases grow in size and complexity—stretching across microservices, third-party libraries, and AI-enhanced features—the attack surface expands. Traditional scanners and siloed testing approaches struggle to keep pace with rapid release cycles and distributed architectures. That gap has driven interest in AI-powered application security tools that layer automation, pattern recognition, and predictive analysis on top of manual reviews and static checks.
Security teams looking to extract value from AI-based AppSec should adopt a few practical practices:
- Shift security left: Put security tooling into the SDLC early so problems are found before code reaches production.
- Combine approaches: Run AI capabilities alongside SAST, DAST, dependency scanning, and manual testing to reduce blind spots.
- Choose platforms that learn: Prefer solutions that improve as they ingest threat feeds, build-time telemetry, and developer feedback.
- Keep humans in the loop: Treat AI as an assistant rather than a replacement; experienced analysts remain essential for complex trade-offs and judgments.
- Map to compliance: Make AI findings mappable to standards such as SOC 2, HIPAA, and GDPR so audit and reporting requirements are met.
Several vendors now position their products around distinct mixes of detection, context, and remediation. Apiiro aims to change how organizations assess and manage risk across the software supply chain by moving beyond legacy scanning toward risk intelligence. Its platform delivers full-stack contextual analysis powered by deep AI, showing not only what vulnerabilities exist in code and dependencies but how code changes, developer actions, and business context interact to shape risk. Apiiro pulls signals from source control, CI/CD pipelines, cloud configuration, and user access logs to prioritize remediation based on business impact.
Mend.io has emerged as a core player in the AI-focused AppSec market, addressing a broad spectrum of risks for engineering teams. The product uses machine learning and advanced analytics to tackle vulnerabilities in code produced by both humans and AI-assisted development. Organizations adopt Mend.io’s unified platform for coverage across source code, open source components, containers, and AI-generated logic. Its tooling goes beyond detection to offer fast, automated, context-aware remediation that reduces engineering effort and lowers business exposure.
Burp Suite remains a staple for web application testers, and its recent AI enhancements add new depth for modern app environments. The product blends classic manual penetration testing techniques with machine learning to produce smarter scanning and richer findings. Legacy DAST (Dynamic Application Security Testing) tools often struggle with highly dynamic or API-heavy applications; Burp’s AI modules adjust in real time, learn from traffic patterns and user behavior, and surface anomalies and subtle vulnerabilities that static checks can miss.
PentestGPT brings generative AI into offensive security, simulating tactics used by contemporary adversaries. Rather than relying only on predefined signatures, the system can craft novel attack paths, generate custom payloads, and explore creative bypasses against controls and protections. The platform mixes autonomous testing with interactive guidance: security analysts, testers, and developers can communicate with the system to receive walkthroughs and hands-on assistance for complex scenarios and real-world exploit development.
Garak focuses on securing AI-infused applications such as large language models, generative agents, and their integrations within broader software systems. As organizations embed AI into customer interfaces, business processes, and automation, new risk vectors have appeared that traditional AppSec tooling was not built to address. Garak probes and hardens these AI-driven interfaces, helping models behave safely and stopping AI-specific exploits like prompt injections and privacy leaks.
Not every product offers the same feature set, but most AI-enhanced application security tools share a set of core capabilities:
- Large-scale pattern recognition: Models trained on extensive exploit datasets can identify coding errors, misconfigurations, and insecure dependencies more accurately than static rule sets, and detection improves as datasets grow.
- Contextual remediation guidance: Finding a flaw remains only half the task; AI can produce remediation advice suited to the codebase and deployment context, often with code suggestions or step-by-step fixes.
- Continuous runtime monitoring: Instead of one-off scans, these tools can observe production behavior, analyze runtime events, API traffic, and data paths to spot anomalies that may signal active attacks.
- Risk-based prioritization: AI assesses exploitability, business impact, and external threat intelligence to rank issues so teams can focus on what poses the greatest harm.
- DevOps integration: Modern AppSec embeds into CI/CD pipelines, issue trackers, and developer environments, and intelligent automation reduces tasks that once slowed builds or required manual handoffs.
AI-based application security is not a single product or a single team’s responsibility; it functions as a platform for more resilient, adaptive software operations. In 2025, market leaders will be those whose systems can learn from new data, align fixes with business priorities, and defend at the speed of modern development. From richer risk intelligence and faster remediation to protections for AI-generated code and agent behavior, today’s AppSec tools are redefining expectations for digital security across industries.