In 2025, trust has become the most used surface in modern computing. For decades, cybersecurity the focus was on vulnerabilities, software bugs, misconfigured systems, and weak network security. Recent cybersecurity incidents have marked a clear turning point as attackers no longer need to rely solely on traditional methods.
This shift was not subtle. Instead, it emerged in almost every major incident: supply chain breaches using trusted platforms, credential abuse in federated identity systems, misuse of legitimate remote access tools and cloud services, and AI-generated content escaping traditional detection mechanisms. In other words, even well-configured systems can be abused if defenders assume that trust equals security.
Highlighting the lessons learned in 2025 is essential for cybersecurity professionals to understand the changing threat landscape and adapt strategies accordingly.
The perimeter does not matter: the threat vector is trust.
Organizations have discovered that attackers exploit assumptions as effectively as vulnerabilities by simply borrowing trust signals that security teams have overlooked. They entered the environment using standard developer tools, cloud services, and signed binaries that never included strict telemetry or behavioral controls.
The rapid growth of artificial intelligence in enterprise workflows has also contributed to this. From code generation and operations automation to business analytics and customer support, artificial intelligence systems have begun to make decisions that were previously made by humans. This has led to the emergence of a new category of risk: automation, which inherits trust. without verification. Result? A new class of incidents in which attacks were not high-profile or overtly malicious but were associated with legitimate activity, forcing defenders to rethink what signals matter, what telemetry is missing, and what behavior should be considered sensitive, even if it occurs over trusted paths.
Identity and autonomy took center stage
Identity also defines the modern attack surface beyond security vulnerabilities. As more services, applications, AI agents, and devices operate autonomously, attackers are increasingly targeting identity systems and trust relationships between components. Once an attacker has a trusted identity, he can operate with minimal difficulty, which expands the meaning of privilege escalation. Escalation wasn't just about getting higher system permissions; it was also about using an identity that others naturally trust. With attacks targeting personal data, defenders have realized that the default distrust must now extend beyond network traffic to workflows, automation, and decisions made by autonomous systems.
AI as a power tool and pressure point
AI acted like protective accelerator and a new frontier of risk. Generating code using artificial intelligence speeded up development, but also led to logic errors when models filled in gaps based on incomplete instructions. AI attacks have become more personalized and scalable, making phishing and scam campaigns more difficult to detect. However, the lesson was not that AI is inherently unsafe; the point is that AI amplifies any control (or lack of control) that surrounds it. Without verification, AI-generated content can be misleading. Without guardrails, AI agents can make risky decisions. Without supervision, AI-driven automation can lead to unintended behavior. This highlights that AI security is more about the entire ecosystem, including LLMs, GenAI applications and services, AI agents and underlying infrastructure.
Shift towards management autonomy
As organizations increasingly rely on artificial intelligence agents, automation platforms, and cloud-based identity systems, security will shift from correcting errors to controlling decision paths. We will see the following defensive strategies in action:
- AI Control Plane Security: Security teams will create layers of control around AI agent workflows, ensuring that every automated action is authenticated, authorized, tracked and reversible. The focus will be expanded from data protection to conduct protection.
- Data drift protection: AI agents and automated systems will increasingly move, transform, and replicate sensitive data, creating the risk of silent data sprawl, shadow data sets, and unanticipated access paths. Without clear traceability of data and strict access controls, sensitive information can flow beyond approved boundaries, leading to new privacy, compliance, and disclosure risks.
- Testing trust at all levels: Expect widespread adoption of “trust-minimized architectures” in which identities, artificial intelligence outputs, and automated decisions are constantly verified rather than implicitly accepted.
- Zero Trust as a Compliance Requirement: ZTA will become a regulatory requirement for critical sectors, with managers facing increased personal liability for serious breaches related to poor safety performance.
- Behavioral foundations for artificial intelligence and automation: Just as user behavior analytics developed for human accounts, analytics will evolve to establish expected patterns for bots, services, and autonomous agents.
- Protected Identity Information: Identity platforms will prioritize strict lifecycle management of non-human identities, limiting the damage when automation goes wrong or gets hacked.
- Intent-based discovery: Because many attacks will continue to use legitimate tools, detection systems will increasingly look at the reasons behind an action, not just what happened.
If 2025 taught us that trust can be used as a weapon, then 2026 will teach us how to rebuild trust in a safer and more informed way. The future of cybersecurity is not just about protecting systems, but also about ensuring the logic, identity and autonomy that govern them.
Aditya K. Sood is vice president of security engineering and artificial intelligence strategy at the company Aryaka.
