Key Trends for Software Executives in 2026

Traditional approaches to vulnerability management and security testing are undergoing significant changes due to the active implementation of AI in cybersecurity. Protection models that previously worked are gradually losing effectiveness and are being replaced by new AI-oriented approaches. It becomes a tool for both attackers and defense teams: the former use AI to automate and scale attacks, the latter to improve detection and response quality.

To stay ahead of threats, organizations need to invest in AI-based vulnerability management solutions and predictive analytics. Such tools allow for analyzing large volumes of data, finding hidden patterns, and detecting potential risks before they turn into incidents.

AI fundamentally changes the approach to application security. Organizations that do not adapt to these changes risk losing resilience to modern threats. That is why it is important for cybersecurity leaders to bet on AI solutions and invest in the necessary skills and technologies today.

One of the key factors influencing the use of open source and software supply chain security will be the further strengthening of regulatory requirements and cybersecurity standards, especially in the fields of artificial intelligence and software security.

The European Union’s quest for digital sovereignty, as well as the implementation of regulations such as the EU AI Act, will significantly impact organizations operating in the EU or collaborating with the European market. Meanwhile, there is growing regulatory attention in the US towards cybersecurity, AI, quantum technologies, and supply chain security, evidenced by new government initiatives and requirements.

The active implementation of AI in cybersecurity creates both new opportunities for protection and additional risks. Organizations will need to simultaneously adapt their security practices and comply with rapidly changing standards, including NIST SP 800-218.

To stay ahead, engineering and security team leaders should focus on implementing AI solutions for security, strengthening software supply chain control, and investing in necessary skills and technologies. This will enhance organizational resilience and improve the ability to detect and neutralize new threats.

Agent AI, which involves autonomous systems capable of complex decision-making and adaptation, is now transforming various industries. In secure software development, agent AI can enhance safety by autonomously detecting threats and responding to them in real time.

With the advent of quantum computing, the need for quantum-resistant cryptography is becoming increasingly critical. Organizations must start transitioning to cryptographic systems capable of withstanding quantum attacks. This involves identifying and classifying high-value, long-term confidential data and assessing the quantum resilience of vendors.

Post-quantum cryptography (PQC) is becoming a critical technology for protecting data infrastructure. The European Union has already launched a coordinated initiative for member states to transition to PQC by 2030. Organizations should already start transitioning to PQC standards, auditing their cryptographic assets, and investing in future security systems.

Edge AI, processing data locally on devices, will improve real-time decision-making and reduce latency. Neuromorphic computing, inspired by the human brain, will further advance the capabilities of edge AI, making devices more efficient and adaptive.

In 2025, companies continued to grapple with several key challenges in secure development. The advancement of AI and generative AI has fundamentally changed the threat landscape and, as a result, has significantly impacted secure development practices.

One of the key issues is the increasing complexity of AI-based attacks, so it is critical for development teams to integrate robust security measures directly into their workflows. In addition, ensuring the security of AI systems throughout their lifecycle remains a critical challenge. This includes not only the secure development of AI solutions, but also protecting models, particularly LLMs, from vulnerabilities such as data poisoning or prompt injection. At the same time, traditional security measures, such as monitoring, logging, and intrusion detection, also remain necessary for effective AI system management.

Supply chain attacks remain a significant threat. Compromising software components, whether open source or commercial, can have critical consequences. Organisations must prioritise the management and monitoring of software supply chain risks, including the use of Software Bills of Materials and careful patch management.

The proliferation of cybersecurity regulatory requirements adds an additional layer of complexity. Organisations have to operate in a fragmented environment of regional and global standards, making it difficult to ensure compliance and security of development processes simultaneously.

Experts predict that a key competitive advantage in AppSec and secure software engineering will be the ability to effectively leverage AI and machine learning (ML) security capabilities. The increasing complexity of AI-based attacks and the necessity to protect AI systems will require organizations to invest in AI management and security specialists, as well as ML experts.

Demand will grow for professionals who can develop and implement AI models while ensuring their security. As the use of cloud technologies increases, expertise in cloud security and knowledge of Zero Trust principles are becoming critically important to protect against credential-based attacks.

One of the most underestimated areas of risk in the software development lifecycle is AI system security. While organizations are actively implementing AI, many do not pay enough attention to such potential threats. AI models can be vulnerable to attacks such as data poisoning, model inversion, and evasion attacks. Furthermore, the complexity of models makes it difficult to understand the decision-making process, complicating risk identification. AI systems also require specialized testing and validation to ensure their safe operation.

To avoid these risks, leaders are advised to establish clear policies and standards for the development and deployment of AI systems, including requirements for security testing and validation. It is worth using specialized AI security tools: for example, to detect vulnerabilities and invest in training and retraining teams to build the necessary expertise.

In secure coding, AI-driven tools will become commonplace for code review, helping to catch vulnerabilities at early stages. They will be able to suggest and automatically apply fixes for common issues, reducing the burden on developers.

In vulnerability management, AI agents will analyze codebases, predict risks, and assist in making proactive decisions. Autonomous systems will also optimize the patch management process — from identifying vulnerabilities to testing and deploying updates.

In development workflows, AI will integrate into development environments, providing real-time feedback and automation. This will enhance productivity and allow security checks to shift-left to earlier stages.

Implement an AI application security testing (AST) strategy and integrate it into DevSecOps. This means using AI to detect vulnerabilities, predict threats, and automate fixes. AST tools should work directly in the development pipeline to ensure continuous monitoring and rapid risk mitigation.

The year 2026 will be pivotal. AI will impact all aspects of cybersecurity—from strengthening defenses to the emergence of new attack vectors. In this context, there are three key requirements that should become a priority for organizations:

· Proactively adopt AI-based security measures.
· Enhance software supply chain security practices.
· Form a team with expertise in AI security and machine learning.

Regulatory trends, particularly around AI and supply chain security, demand vigilance. By integrating AI into development workflows and implementing a comprehensive AI-based AppSec strategy, organizations will actively shape a more resilient and secure digital future.

Thanks to the integration of AI into development processes and the implementation of a comprehensive AI-based AppSec strategy, organizations will actively shape a more resilient and secure digital future.