October is Cybersecurity Awareness Month, an annual initiative to remind us all to keep online security top of mind. The Cybersecurity and Infrastructure Security Agency (CISA), the federal agency responsible for helping protect U.S. critical infrastructure from cyber and physical threats, leads the campaign. Its 2026 theme, “Securing the Next 250,” connects the push to build a secure digital future with the nation’s 250th anniversary.
For the software and security teams supporting those efforts, AI is changing both how applications are built and how they can be attacked. Agentic AI, which can carry out multistep tasks with limited human direction, adds further concerns about what automated systems can access and do. Protecting the software behind essential services means securing AI-enabled development workflows and preparing for increasingly automated threats to the applications they produce.
Many of today’s software security threats are the same as yesterday’s. Insecure code, vulnerable dependencies, exposed credentials, and misconfigured systems have challenged teams for years. Now, however, AI is amplifying those challenges as teams generate more code and move it through development faster. Respondents to the 2026 Sembi Software Quality Pulse Report estimate that an average of 53% of their organizations’ code is now AI-generated or AI-assisted. That enhanced productivity can also bring on security concerns.
AI introduces security risks across the SDLC. Generated code can contain insecure patterns or unvetted dependencies, and developers may expose proprietary code or credentials to external model APIs. Malicious inputs can also influence AI-assisted review and triage tools. When agentic tools have permission to act, that influence can result in an unauthorized repository change or an unsafe pipeline command. The risk depends on both the information a tool receives and what it is allowed to do.
Software that passes pre-release checks can still face risks when attackers obtain a copy or control its execution environment. This can happen on a customer device, in a browser, or on a partner’s server. They may modify application behavior, remove licensing checks, disable protections, or redistribute altered versions. This post-deployment security gap persists even when development-stage checks have found and resolved known vulnerabilities.
Decompilers and debuggers help attackers inspect an application’s structure and behavior. AI can help interpret that output, summarize unfamiliar functions, trace dependencies, and locate sensitive logic. AI can reduce the expertise and effort required to investigate proprietary algorithms, authentication workflows, or business rules. Attackers may find ways to copy or manipulate that functionality without first discovering a conventional vulnerability.
AI in cloud security helps defenders correlate logs, detect unusual account activity, and prioritize risky configurations. Attackers can also use it to investigate exposed services, analyze APIs, and refine credential phishing. AI-enabled workflows introduce additional risks: prompt injection or poisoned data can mislead analysis, while excessive permissions can lead to mistaken decisions that affect live systems. An automated response tool, for example, could misidentify a legitimate account as compromised and disable it.
Addressing these risks requires coordination, but many teams still rely on disconnected security tools and processes. The 2026 Sembi Software Quality Pulse Report finds that only 9% of security teams report that their toolchains are fully integrated with DevOps workflows.
If that sounds familiar, you can strengthen your defenses by identifying coverage gaps, integrating controls into existing workflows, and automating repetitive security tasks.
Review coverage across code, dependencies, APIs, cloud services, and runtime behavior to identify application security gaps. Look for manual handoffs, excessive AI tool permissions, and applications lacking post-deployment protection. Prioritize improvements by exposure and business impact, including potential disruption to essential services. And don’t forget to include cloud configurations, service-account permissions, and approval safeguards for automated responses in that review.
AI-assisted development is likely here to stay, so apply the same security standards to AI-generated code as you would code that’s human-written. SAST, SCA, pre-commit secrets scanning, dependency checks, and human review should all be part of the process. Govern AI tools’ access to repositories, sensitive data, and production systems. Limit autonomous tools’ permissions, define actions requiring approval, and treat external inputs as potentially untrusted.
Application hardening and code obfuscation help protect the intellectual property embedded in your software by making your application logic harder to reverse-engineer and modify. Pair obfuscation with tamper detection to identify unauthorized changes, plus anti-debugging and runtime protections to help detect or impede analysis and manipulation during execution.
Cybersecurity Awareness Month is a good time to take stock of your security practices, identify gaps, and assess your overall security posture. What matters is carrying those improvements into everyday work, so protection keeps pace with software changes and emerging threats throughout the year.
PreEmptive helps you make application hardening part of that routine. By integrating these protections into build and release workflows, you can address post-deployment risks before software reaches an attacker’s hands.
Book a live demo and learn how PreEmptive can help make application protection part of every release.