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AI Code Agent
On-demand Webinar
On-demand Webinar

Overcoming the AI Velocity Paradox in Security | Harness Resource

AI-driven development is transforming software engineering. Organizations are reporting up to 25% faster code generation and 70% of developers shipping code more frequently. However, while development velocity accelerates, security processes lag, creating a dangerous paradox: AI speeds up code delivery, but security cannot keep pace.

According to the latest State of AI in Software Engineering report by Harness:

  • 48% of organizations worry that AI coding assistants are introducing more vulnerabilities into software.
  • Only 46% of security and compliance workflows are automated—compared to 51% for coding workflows.
  • 43% are concerned about regulatory non-compliance stemming from untested or insecure AI-generated code.

The widespread adoption of AI tooling has opened entirely new attack surfaces—ones that traditional AppSec methods were never designed to handle. Organizations must now defend against:

  • Vulnerabilities introduced by generative AI
  • Prompt injection attacks that hijack model behavior
  • Data poisoning of training datasets
  • Model extraction attacks targeting proprietary IP
  • Adversarial inputs designed to mislead AI systems

To address these challenges, AI red teaming is emerging as a key practice—but current approaches are fragmented and inconsistent.

In this information-packed webinar, we’ll explore how to overcome the AI velocity paradox in modern software security. Join us to learn:

  • Gain end-to-end visibility into your AI ecosystem — from APIs to MCP tools and sensitive data flows.
  • Build resilient AI defenses by uncovering runtime vulnerabilities and compliance risks early.
  • Stay ahead of AI threats with real-time detection of prompt injections, model misuse, and input explosions.
  • Secure AI delivery faster by shifting left to eliminate OWASP LLM Top 10 risks before release.

Whether you're an engineering leader, AppSec professional, or AI architect, this session will equip you with the frameworks and tools needed to keep pace with AI-driven innovation—without compromising on security.

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Published
November 5, 2025

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What you'll learn

Key Takeaways

AI Agents Introduce New Decision Risks

Unlike traditional deterministic applications, AI agents and LLMs introduce non-deterministic decision risks. Organizations must strictly control data access and avoid granting excessive permissions to mitigate this expanded attack surface.

Integrate AI Red Teaming Into the SDLC

Embed dynamic application testing and ethical hacking directly into continuous integration pipelines. This allows teams to automatically identify vulnerabilities like prompt injection and model poisoning before pushing releases to production.

Combine Regex With AI Data Classification

Traditional regex patterns are insufficient for tracking sensitive data hidden within large language model prompts. A robust architecture must combine regex with AI-powered data classification to monitor information leaving the application perimeter.

Track Detection and Containment Metrics

To measure AI security health without hindering development velocity, organizations should focus on real-time monitoring. The most critical metrics to track are the mean time to detect an attack, mean time to contain it, and the blocked injection rate.

Adopt a Phased AI Security Roadmap

Security teams dealing with shadow AI and documentation blind spots should begin by establishing complete visibility into their AI assets. From there, organizations can mature their posture by advancing to comprehensive testing and eventual runtime protection.