2025 Trends on AI Security: How AppSec Must Evolve with the AI-Shifted SDLC

The software development lifecycle (SDLC) is no longer linear—and it’s no longer just human. In 2025, AI is embedded across every stage of the SDLC, from AI code assistants that generate entire modules to autonomous agents handling QA and deployment. Today, nearly three-quarters (72%) of organizations use AI for code generation and 67% use it for code documentation and review. This means that AI isn’t just an execution aid—it’s increasingly core to planning, design, and architecture.

While this transformation brings new efficiencies, it also creates unfamiliar attack surfaces, deeper code complexity, and real-time security decision points. This blog explores the top trends on AI security in 2025 and how application security (AppSec) must evolve to secure this new AI-augmented development landscape.

What Is the Difference Between Generative AI and Agentic AI?

Before we explore the top AI security trends, it’s important to first make the distinction between generative AI and agentic AI. Generative AI (like GitHub Copilot or ChatGPT) produces content or code based on input prompts but does not take autonomous action. Agentic AI, on the other hand, can autonomously plan, act, and iterate—like self-remediating vulnerabilities or orchestrating tests and deployments. 

This distinction has some critical implications for security teams. For starters, agentic AI pushes teams to consider how much autonomy their AI systems have. Security teams must also ask themselves, “What safeguards are in place to prevent agentic AI from executing malicious or unintended actions?” or “How will we audit and log decisions made by non-human actors?”

What Are the Trends for AI in Cybersecurity?

In 2025, AI security isn’t just about protecting AI models—it’s about rethinking security in a world where AI is building, testing, and shipping software alongside humans. Here are the top four trends reshaping the AppSec world:

1. AI-in-the-Loop Testing

AI-in-the-loop testing is a fast-evolving approach in which artificial intelligence is integrated directly into software testing workflows—not just as a passive tool, but as an active collaborator in designing, executing, and analyzing tests. Teams are increasingly using AI for:

• Test Case Generation: AI analyzes code, architecture, or past bug data to auto-generate test cases—often covering edge cases that humans miss. For example, LLMs like GPT-5 can convert user stories into test scenarios automatically.
• Input Fuzzing and Behavior Simulation: AI injects unexpected, malformed, or adversarial inputs to simulate user behavior or uncover vulnerabilities. This is especially common in security testing and chaos engineering.
• Autonomous Test Execution: AI agents schedule and run tests based on code changes, risk level, or dependency analysis. Autonomous test execution is often integrated into CI/CD pipelines.
• Intelligent Result Triage: AI clusters and prioritizes test failures based on severity, novelty, and historical fix data—saving developers from reviewing false positives.
• Adaptive Feedback Loops: AI learns from each test cycle, continuously refining test strategies, especially in systems that evolve quickly (like ML pipelines or microservices).

There are a number of benefits to AI-in-the-loop testing. AI can test code paths and permutations that humans overlook and deliver near-instant feedback to accelerate code development and deployment. It also predicts which areas of code are likely impacted by new changes and targets those areas, reduces the need for massive human QA teams, and simulates malicious behavior to identify security flaws earlier on in code development.

However, with those advantages come risks. Models are only as good as their training data, so if that data is biased or insufficient, AI may miss bugs. AI can also become an attack vector itself. Common AI threats include prompt injections, model manipulation, and data poisoning in training loops.

To combat these risks, security teams should use human-in-the-loop review for high-risk areas and maintain logs and explainability for AI-generated or selected test cases. They can also apply differential testing to compare AI-generated output against baseline behavior and build a developer feedback loop to train AI with verified good/bad test outcomes. Finally, it’s important to follow Zero Trust fundamentals, such as limiting AI permissions in CI/CD to avoid full access to environments or production systems.

AI-in-the-loop testing is a powerful enabler of speed, scale, and adaptability, but it must be implemented with clear visibility, human oversight, and secure boundaries. It’s not a replacement for skilled QA—it’s a force multiplier when used responsibly.

2. Autonomous Remediation 

Autonomous remediation using agentic AI is one of the most transformative—and controversial—developments in software security and DevOps. It refers to the ability of AI agents to autonomously detect, diagnose, and fix issues in code, infrastructure, or configurations without requiring human initiation at each step. These goal-oriented agents can:

• Identify security flaws in CI/CD pipelines
• Open pull requests with fixes
• Trigger new test runs autonomously

Typically, the agent will detect a new CVE, failing test, or code regression via logs, monitoring, or static analysis. Then, it analyzes the issue—reviewing logs, dependency graphs, or blame history to pinpoint the source. Next, it determines a viable patch or rollback. This might include code changes, config updates, or access control revisions. Once the agent has implemented the fix (usually as a PR or scripted change), it initiates a test run and evaluates whether the fix resolves the issue and introduces no new bugs (e.g., via test suites or runtime telemetry). If validated, the change is merged and deployed (autonomously or after human sign-off).

Autonomous remediation with agentic AI represents a paradigm shift in how we manage software security and reliability. It introduces enormous efficiency but demands new kinds of oversight, safeguards, and cultural maturity. Unlike a human developer, AI often lacks full context about why a line of code was written a certain way, which business constraints it needs to account for, and long-term architectural intentions. And because agentic systems with write access can change files, modify infrastructure, or trigger deployments, it’s important to make sure they’re configured properly and protected against AI-specific threats like prompt injection or model poisoning.

Done right, autonomous remediation transforms DevSecOps from reactive firefighting into proactive, self-healing systems. But done recklessly, it can introduce silent and systemic risk at machine speed. Leading teams are implementing “human-in-the-loop” oversight models to ensure safe remediation without introducing regressions.

3. Vibe Coding 

“Vibe coding” is a relatively new phenomenon that has emerged from the rise of AI-assisted programming tools like GitHub Copilot, ChatGPT, and other LLMs. It describes a shift in how developers interact with code: less like writing precise instructions, and more like collaborating with an AI partner through trial, error, and iteration.

This represents a departure from traditional intent-driven programming, where developers carefully architect, design, and build each line of code with a clear mental model. Instead, vibe coding leans heavily on:

• Generating code from natural language prompts
• Editing AI suggestions until the output “works”
• Relying on intuition and “vibes” rather than deep comprehension

While this approach is often faster and more fluid, it introduces some serious tradeoffs. Some of the main risks with vibe coding include:

• Shallow Understanding: Developers may use code they don’t fully understand, which can lead to security, reliability, or performance issues.
• Debugging Difficulty: Fixing bugs in AI-generated code can be harder if you didn’t write or follow the original logic.
• Security Blind Spots: AI-generated code may include hidden vulnerabilities, insecure defaults, or poor validation.
• Technical Debt: Code that “works for now” might not scale, comply with standards, or integrate cleanly with the broader system.
• Overreliance on AI: Developers might lose touch with fundamental skills, such as algorithm design, architecture thinking, or code optimization.

Vibe coding is both a shortcut and a symptom of a new development culture—one where AI co-pilots are deeply embedded in how we create software. It offers speed and creativity at the expense of control and understanding. In an AI-shifted SDLC, the key is knowing when to vibe and when to verify. While vibe coding can be useful for hackathons or rapid prototyping, it’s not suited for mission-critical systems, highly regulated applications, security-sensitive components, or highly collaborative teams that need code clarity and consistency. In a world of vibe coding, AppSec must now prioritize continuous code analysis and contextual guardrails that operate at the speed of prompting.

4. Evolving CI/CD Pipelines and Developer Workflows

To keep pace with AI-augmented development, modern AppSec must evolve from a gatekeeper model to a continuous, contextual, and developer-embedded strategy. As developers increasingly rely on generative AI, autonomous agents, and dynamic CI/CD pipelines, AppSec teams must rethink how and where they operate in the SDLC. This includes:

• Shifting from Point-in-Time to Continuous AppSec: Traditional AppSec practices rely on periodic scans and manual reviews, which are ineffective in fast-moving, AI-assisted CI/CD environments. Instead, teams should integrate real-time static and dynamic analysis directly into integrated developer environments (IDEs) and pipelines. Use tools that continuously monitor for vulnerabilities post-deployment, such as runtime application self-protection (RASP), and enable always-on security telemetry that feeds into AI-driven threat models and behavioral baselines.
• Embedding Security into Developer Workflows: Developers now work across AI tools, IDEs, prompt libraries, and autonomous code suggestions, which often fall outside traditional AppSec visibility. To adapt, teams can embed security linter extensions in IDEs and LLM copilots (e.g., secure-by-default code completions) and use developer-focused security tools that speak the language of engineers. AppSec teams should also provide real-time feedback on insecure code patterns as they’re written—not in post-commit gates.
• Accounting for AI-Generated and Agentic Code: Code isn’t always authored by humans anymore. AI copilots, agents, and LLMs generate code and configurations that may bypass secure coding practices. In response, teams should mandate provenance tracking of AI-generated code and require security review of agent-suggested PRs—especially in critical or sensitive areas. It’s also important to train AppSec tools to scan for AI-specific vulnerabilities, like insecure prompt chaining, injection vectors, or model misconfigurations.
• Modernizing Threat Modeling and Risk Assessment: Traditional STRIDE-based threat modeling may miss new risks introduced by autonomous agents, third-party LLMs, and non-deterministic AI outputs. By adopting AI-specific threat modeling frameworks (e.g., the MITRE ATLAS knowledge base) and prioritizing threats based on real-time risk scoring, teams can better counter AI-specific threats.
• Expanding SBOMs to Include AI Artifacts: A secure software supply chain isn’t just about third-party libraries anymore. AI artifacts—models, prompts, weights—must also be versioned, verified, and audited. Teams must extend Software Bills of Materials (SBOMs) to track model hashes and source, fine-tuning data lineage, agent configurations and permissions, and prompt versions and templates.
• Enforcing Policy-as-Code and AI-Aware Guardrails: Security gates must scale across thousands of autonomous actions triggered by AI agents in CI/CD. Policy-as-Code rules must be able to govern which models can be deployed, who can approve agent PRs, and which prompts are authorized. Teams can also integrate automated policy checks in CI/CD and add security test stages for LLM outputs, such as prompt behavior validation or adversarial testing.
• Investing in Developer AI Education: Developers need new skills to spot security flaws in AI-assisted environments, including vibe-coded and agent-written code. By updating secure coding training to cover topics like prompt engineering best practices or agentic behavior audits and guardrails, developers are better positioned for success in this new AI-enabled SDLC.
  

How Can AI Be Used in Security

AI is not just a risk—it’s also a powerful ally. Leading security teams are using AI security solutions to:

• Identify vulnerable code snippets in real-time as developers type
• Score risk levels of AI-generated pull requests
• Detect behavioral anomalies across development, staging, and production environments
• Automate compliance checks across infrastructure-as-code, APIs, and user permissions

AI is augmenting AppSec—not replacing it.

Is AI Going to Replace Cybersecurity?

No—but it is redefining it.

While AI agents are becoming more capable, human oversight remains critical. The role of cybersecurity professionals is evolving to:

• Architect resilient AI-integrated systems
• Monitor and fine-tune AI behavior
• Create frameworks for AI governance, auditing, and incident response

Ultimately, we believe AI will replace repetitive tasks, not strategic security thinking. Forward-thinking teams are embracing this shift with:

• AI-aware threat modeling: Identifying where AI intersects with infrastructure, code, and logic flows
• Security training for AI copilots: Injecting AppSec rules into the LLMs that developers use
• Adaptive pipelines: Incorporating runtime security feedback into CI/CD flows, not just pre-deployment scans
• Zero-trust AI agents: Treating every AI output as untrusted until verified

The state of AI security in 2025 demands new muscle memory—from developers, security engineers, and platform teams alike. 2025 marks a turning point in AppSec. As AI reshapes how code is written, tested, and deployed, the security mindset must shift from control points to continuous context.

At Checkmarx, we’ve created an entire family of AI cybersecurity agents that support your entire team and integrate seamlessly into your DevOps environments. Developer Assist Agent is an AI secure coding assistant, trained to prevent and remediate vulnerabilities as you code in VSCode, Cursor, and Windsurf IDEs and pipelines. We’ll also soon be releasing an AI Devops agent that can continuously scan, prioritize, and fix vulnerabilities across your CI/CD pipeline, as well as an AI-powered AppSec Insights Agent to provide live visibility into AppSec posture, risk trends, and SLA adherence.

Modern AppSec isn’t about checking boxes—it’s about building intelligent guardrails that evolve alongside your AI-augmented SDLC. With our always-on layer of AI-powered defense, your teams can focus on building, not fixing. Don’t be one of the 83% of enterprises that ship AI-assisted code without sufficient AppSec controls. Discover how the Checkmarx One Assist platform transforms AppSec with intelligent, autonomous protection across the SDLC.