Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Dirk Schrader, Vice President of Security Research, Netwrix
The next wave of cybersecurity disruption will be driven not by new tools but by new dependencies. Identity becomes code, behavior becomes control, insurance becomes regulation, and AI becomes the connective tissue between digital and physical systems. As these shifts accelerate, the organizations that thrive will be those that treat security not as infrastructure, but as a living ecosystem of automation, economics, and human behavior. The following predictions outline the most likely, plausible, and outright disruptive changes that will shape cybersecurity between 2026 and 2029.
What’s Most Likely to Reshape Security by 2026
1. Identity Automation Becomes the Next Attack Surface
Identity is becoming the system of record for trust, and that shift brings its own fragility. By 2026, identity frameworks will be fully automated in leading organizations: machine accounts managing other machines, tokens issuing tokens, and APIs negotiating privileges at machine speed. The control plane itself will need monitoring. But some organizations will remain in that half-manual danger zone.
Attackers are pivoting from password theft to identity orchestration abuse, targeting provisioning workflows, federation protocols, and misconfigured automation. Compromise one identity broker, and access can be silently minted across entire hybrid ecosystems. The next generation of breaches will go far beyond just stealing credentials. They will exploit the code that grants identity itself.
For defenders, 2026 is not about adopting Identity and Access Management (IAM) but about auditing the automation behind it. Continuous validation of identity logic, who issues credentials, under what policy, and with what dependencies, will become essential. Identity Threat Detection and Response (ITDR), Privileged Access Management (PAM), and Data Security Posture Management (DSPM) must converge into a self-monitoring identity fabric that can detect misuse.
2. Behavioral KPIs Replace Awareness Training
Cybersecurity awareness will stop being an annual ritual and become a living behavioral metric. Instead of quizzes and simulated phishing, organizations will measure what employees actually do: Multi-Factor Authentication (MFA) response times, phishing-report rates, data-handling patterns, and incident reaction speed.
This shift signals maturity. People are not “the weakest link” but dynamic agents whose behavior can improve through visibility and feedback. With analytics and AI, awareness becomes adaptive, nudging users in real time rather than lecturing after mistakes.
Ethics matters. Continuous measurement must remain transparent; surveillance disguised as security will erode trust faster than any breach. Security teams should pilot behavioral dashboards and co-design privacy safeguards with Legal and HR to prevent pushback while defining measurable metrics. If these metrics are used to punish rather than to coach, adoption will collapse. Design them as a mirror, not a whip.
The cultural upside is clear. Security becomes part of daily performance, not an annual interruption. Organizations that succeed will turn human behavior into their most responsive control, security as culture, not compliance.
3. Cyber Insurance Turns into Continuous Compliance
By 2026, cyber insurance will evolve from a passive safety net to a real-time regulator. Rising claims and stricter disclosure laws will push insurers to demand continuous proof of control performance, not static questionnaires.
Through standardized APIs, underwriters will receive telemetry from clients’ security platforms, Security Information and Event Management (SIEM) logs, PAM dashboards, MFA coverage reports, and adjust premiums and coverage based on live posture. Companies with verified controls and tested recovery plans will pay less, while those without may pay more or lose coverage entirely. In heavily regulated sectors, this will likely start with posture scores and attestation APIs rather than raw log streaming, simply because regulators and works councils won’t accept more.
This model ties economic value directly to resilience. Security becomes a measurable business asset, not a cost center. Teams should prepare by automating compliance evidence, adopting vendor-neutral data standards, and defining governance for data sharing.
Cyber insurance will not just pay for resilience; it will price and enforce it. Organizations that can support automated transparency will earn market rewards, while the rest will face uninsurable risk.
What’s Next on the Horizon by 2027
4. AI-Driven OT/IT Convergence
AI is erasing the line between IT and operational technology (OT). Predictive maintenance, smart manufacturing, and energy optimization link factory sensors to cloud analytics and corporate networks. Efficiency gains are massive, but so is the new attack surface where code meets machinery.
By 2027, we may see the first major AI-mediated cross-domain breach, a compromise that starts in IT and cascades into physical systems via shared AI models and telemetry pipelines. The threat isn’t just downtime; it’s physical impact.
Defenders must build joint governance between CISOs and plant managers. Machine identities, APIs, and telemetry flows require the same Zero-Trust rigor as user logins. OT segmentation must evolve into logical control boundaries defined by data flow and model ownership. The real risk to address is that one shared model or telemetry pipeline becomes the bridge that no firewall rule anticipated.
Securing this convergence becomes foundational. In an AI-integrated world, operational continuity and cybersecurity become one discipline.
5. Data Becomes Self-Protecting
The next revolution in data security is self-protection, information that carries its own encryption, access policy, and provenance. Files, messages, and APIs will travel in policy-bound formats with cryptographically enforced access rules.
This shift makes compliance automatic. The control travels with the content but requires close collaboration between security engineers and data-governance teams. Policy designers must think like coders, and cryptographers must design for usability.
The promise, breaches lose impact because stolen data remains locked or watermarked. The challenge, interoperability. Without open standards, self-protecting data could become siloed.
Security leaders should begin evaluating policy-aware formats and encryption frameworks and invest in metadata infrastructure that immutably records access events. When data governs itself, privacy and protection become features, not afterthoughts.
We’ve seen earlier generations of digital rights management (DRM) and information rights management (IRM) systems fail on usability and interoperability. The difference now is better identity plumbing and more mature cloud-native policy engines – still, some of the old pitfalls are waiting.
Beyond 2027: A Wildcard that Could Redefine Everything!
6. The AI Winter of Trust – Economic Retrenchment Erodes Security
If the current AI boom cools, an “AI Winter of Trust” could follow. As funding tightens, companies may cut compliance and security budgets first. The danger lies in orphaned algorithms that keep processing data long after governance teams vanish. Even today, most organizations couldn’t list all the models they run, let alone tell you who owns them or how they’re monitored.
Breaches might come not from hackers, but from models drifting ethically or operationally off course. Firms may skip EU AI Act assessments, reuse sensitive datasets, or fail to document model behavior. Public backlash could set AI adoption back years.
Security teams should map AI dependencies and ownership, which models run where, on whose data, and under what maintenance regime. Continuous validation of model integrity and compliance will become as vital as patch management once was.
Governance is not bureaucracy but a security control. Organizations that maintain oversight during downturns will remain trusted when others fail.
Winning in the Age of Automated Trust and AI Governance
The next era of cybersecurity will be defined not by a single breakthrough but by the collision of automation, economics, human behavior, and AI governance. Identity becomes code, compliance becomes continuous, data becomes self-protecting, and operational technology becomes inseparable from IT. The organizations that win will be the ones that stay adaptable by auditing their automation, measuring behavior ethically, sharing risk transparently, and treating AI governance as a core security function. In an environment defined by speed and uncertainty, resilience becomes the ultimate competitive advantage.
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ABOUT THE AUTHOR
Dirk Schrader is VP of Security Research at Netwrix. A 25-year veteran in IT security with certifications as CISSP (ISC2) and CISM (ISACA), he works to advance cyber resilience as a modern approach to tackling cyber threats. As the VP of Security Research, Dirk is working on focused research for specific industries like healthcare, energy, and finance.
