For any of you who have read any of my prior year-end predictions, you will know that I am actually idling at something near 100%… of misses. Having said that, I am back at it again, wrapping up the last week of 2025.
Application security in 2026 will be defined by AI-written code, AI-powered attacks and defenses, and an aggressive push toward code-to-cloud visibility and automation across the SDLC.
Security teams that successfully embed intelligent, developer-first AppSec into fast-moving, AI-driven delivery pipelines will outperform those that treat AppSec as a late-stage gate.
1. AI will write most code—and AppSec will be forced to match its speed
By 2026, a meaningful share of enterprise application code will be produced or heavily assisted by generative AI, dramatically increasing the volume of code entering pipelines.
At the same time, most organizations will still lack mature governance for AI-generated code, widening the gap between development velocity and security assurance.
Expect three clear consequences in AppSec:
- Secure-by-default AI coding assistants will become a core control, with built-in SAST/SCA-like checks and policy enforcement at prompt and suggestion time. The new “shift left”.
- AppSec programs will prioritize AI-specific policies (model selection, training data exposure, prompt injection protection) as first-class requirements in secure SDLC frameworks. Think “guardrails” and “watchdogs”.
- Metrics will shift from “how many vulns did we find” to “how safely can we scale AI-assisted development,” including guardrail coverage, policy violations prevented, and remediation time for AI-introduced flaws.
2. Agentic AI will become both attacker and defender
Attackers are already experimenting with autonomous agents that can discover targets, chain exploits, and adapt in real time, and these capabilities are projected to become more prominent by 2026.
In parallel, defensive teams are starting to deploy AI-driven engines for anomaly detection, triage, and auto-remediation across application and cloud environments.
For AppSec, this will show up as:
- Autonomous “AppSec co-pilots” that continuously scan code, dependencies, IaC, and runtime behavior, propose fixes, and sometimes open pull requests automatically.
- Policy-aware AI agents that enforce security checks at each pipeline stage (e.g., blocking non-compliant deployments, tagging risky services, or rolling back insecure releases). There might be a step before this in AI policy agents that will police the AI agents, controlling (or maybe tattling on) AI’s that flood repos with a gazillion packages of varying quality and redundancy.
- A new skills mix on AppSec teams, combining deep security expertise with data engineering and AI model governance to tune and monitor these agents.
3. Code-to-cloud visibility and policy will become non‑negotiable
As organizations continue to adopt multi-cloud, microservices, and containerized architectures, the attack surface moves well beyond static source code into configurations, infrastructure, and runtime behavior.
By 2026, leading teams will expect a unified view that ties a running service and its risk posture all the way back to the specific commits, developers, and components that created it. This is all in support of the need for “context” and “provenance”.
Key shifts include:
- Greater convergence of SAST, SCA, IaC scanning, container/image analysis, and runtime signals into integrated platforms with “code-to-cloud” lineage. This is kind of what you know of as ASPM today, but less a dashboard of dashboards and more of an actual predictive/analytical ASPM tool.
- Risk scoring models that blend code-level issues (e.g., injection, hardcoded secrets) with environmental context (public exposure, data sensitivity, identity paths) to drive prioritization. This will all be on top of the context and attribution foundations.
- Stronger collaboration between AppSec and cloud security teams, with shared policies and playbooks for misconfigurations, supply-chain issues, and runtime anomalies.
4. Supply chain integrity and SBOMs will move from a checkbox to an operational practice
High-profile supply-chain attacks and growing regulatory pressure are pushing organizations toward continuous visibility into open-source, third-party, and internal components. By 2026, SBOMs and related artifacts will be widely expected not just as static documents, but as living objects integrated into build, deploy, and response processes. Some of this is EU regulators, but some of it is just basic hygiene, as we keep getting ransomed because of our mess, and AI code gen adds packages like they are candy.
For AppSec programs and products, that means:
- Automated SBOM generation as a default output of builds, spanning source, dependencies, containers, and possibly AI-generated components.
- Runtime validation that only approved and verified components are deployed, with drift detection if images or libraries change outside the pipeline.
- Tighter integration between vulnerability intelligence feeds and SBOMs to enable impact analysis (“Which services are exposed to this CVE, and where are they running?”) in minutes instead of days.
- I will leave it to the student to work out how we go from an alert in minutes to a repair through the backlog feeding a 2-week sprint cadence, however.
5. Regulations and customer pressure will harden AppSec expectations
The application security market is expected to grow significantly through the late 2020s, driven by regulatory demands, breach fatigue, and cloud adoption. At the same time, research shows that organizations still frequently ship known vulnerabilities and suffer code-related breaches, creating pressure for more accountable, auditable practices.
In 2026, expect:
- More contractual and regulatory requirements for demonstrable secure SDLC practices, including training, tooling coverage, and evidence of policy enforcement.
- Increased emphasis on outcome-based metrics (breach reduction, time-to-remediate, coverage of critical applications) in board and customer conversations.
- Growing preference for platforms that combine developer experience, automation, and governance reporting over point tools that operate in isolation.
These dynamics collectively point to an AppSec landscape where intelligent automation, code-to-cloud context, and practical governance are no longer differentiators but expectations, and where products that truly help developers ship secure software at AI-era speed will define the next wave of leaders.
There you go… set up your score cards, sharpen your pencils, and let’s see how I do in 2026—I have a streak to protect!
