Every year, I do a look back and a prediction forward on security trends. I do this to help answer a common question I get at the beginning of each year: what security trends should I be paying attention to?

This year, AI continues to tip the scale. Looking back at 2025, the acceleration was hard to miss — AI-enabled attacks became more convincing, deepfakes more practical, and software development accelerated beyond what many security programs could reasonably absorb. Paired with persistent remote work, expanding access boundaries, and ongoing geopolitical pressures, long-standing security assumptions began to fracture. As we move into 2026, I expect these same dynamics to persist — but with greater sophistication and far less room for complacency.

Here are my top five security trend predictions for 2026:

1. AI Is The Attacker And Defender

What’s changing

AI is moving from “nice to have” to the execution layer for both attackers and defenders. On the offensive side, it’s reducing the cost and skill required to run effective campaigns, and it’s enabling attackers to operate continuously at machine speed.

  • More convincing phishing, vishing, and social engineering content
  • Faster reconnaissance and vulnerability discovery
  • Greater scale in credential attacks and API abuse
  • More adaptive malware and evasion tactics

On the defensive side, the volume of telemetry has grown beyond what people can analyze manually. Even strong teams struggle to keep up with alert volume, let alone correlate weak signals across identity, endpoints, networks, and apps in real time.

Why this matters

The core constraint is increasingly human speed—not tool availability. The strategic question is less “should we use AI?” and more “where do we trust automation and autonomy?” Security teams are being pushed toward an operating model where systems act as first responders and humans supervise higher-risk decisions.

What to focus on

  • Behavior-first detections, not static indicators
  • Automated correlation across identity, network, endpoint, and application signals
  • Clear response boundaries: what can run automatically vs. when humans must approve
  • Ongoing tuning and validation so automation stays reliable over time

2. Identity Remains The Perimeter

What’s changing

The “trusted internal network” is disappearing. Apps live across multiple clouds and SaaS platforms. Users work from anywhere. Services are exposed directly to the internet via APIs. In this environment, identity becomes the primary enforcement point for users, services, and increasingly autonomous systems.

Meanwhile, passwords remain one of the most fragile links in the chain:

  • They’re easily phished and frequently reused
  • MFA can be bypassed through fatigue attacks and session hijacking
  • Bots test leaked credentials at massive scale
  • Tokens and cookies are stolen and replayed

Why this matters

Once identity is compromised, many downstream controls become less relevant. Valid credentials let attackers blend in and move laterally. This is no longer just a “user problem,” either—non-human identities (service accounts, automation tools, APIs, and AI agents) now represent a large share of access events and often have broad, poorly monitored privileges.

What to focus on

  • Phishing-resistant authentication (e.g., FIDO2 / WebAuthn)
  • Reducing or eliminating password reliance wherever feasible
  • Treating identity as a continuously evaluated signal—not a one-time event
  • Least privilege for both human and non-human identities
  • Behavioral monitoring for identity anomalies, not just login failures

3. Universal Zero Trust

What’s changing

Zero Trust started (for many) as a response to remote access. But modern environments need consistent access decisions well beyond user-to-application traffic, including service-to-service communication, cloud workloads, development pipelines, and AI tools touching enterprise data.

Why this matters

As environments grow, exceptions accumulate. Temporary access becomes permanent. Visibility degrades. Security teams lose confidence in who can access what—and why. Usability also becomes a security issue: when access is slow, brittle, or confusing, people route around controls.

What to focus on

  • Identity-based access decisions for all resources
  • Device posture and context as part of authorization
  • Continuous verification rather than static allow lists
  • Secure access models for APIs and workloads
  • Consistent policy enforcement regardless of location

4. Resilience Over Prevention

What’s changing

Organizations now depend on complex, interconnected ecosystems—cloud providers, SaaS platforms, third-party scripts, external APIs, integrations, and managed service providers. In that reality, even strong preventive controls can’t eliminate all failure modes. Disruptions and inherited risk become part of normal operations.

Why this matters

Stakeholders increasingly judge security by operational outcomes: availability, time to detect, time to contain, time to recover, and how transparently incidents are handled. “No incidents ever” isn’t the standard—continuity and recovery are.

What to focus on

  • Designing systems to degrade gracefully under stress
  • Capacity planning for spikes and attack scenarios
  • Redundancy and geographic distribution where appropriate
  • Tested incident response and recovery playbooks
  • Automation that reduces time to containment and restoration

5. AI And Security Governance Converge

What’s changing

AI adoption is moving faster than governance frameworks can keep up. Tools get adopted organically, data moves across systems, and visibility often lags behind usage. This creates subtle risk that doesn’t always look like a traditional breach.

  • Sensitive data exposure through AI interactions
  • Prompt injection and unintended data leakage
  • Autonomous systems acting without appropriate oversight
  • Regulatory exposure without clear intent or enforceable controls

Why this matters

Organizations will be expected to demonstrate technical enforcement of governance—not just policy documents. That means being able to show where data lives, who (and what) can access it, how AI systems interact with it, and how activity is logged and reviewed.

What to focus on

  • Data classification and access control as the foundation
  • Visibility into AI usage and enterprise data flows
  • Guardrails around model inputs and outputs
  • Monitoring for anomalous access patterns and silent leakage
  • Governance implemented through systems, not documentation

Closing

These trends point to a common conclusion: modern security is less about individual tools and more about intentional architecture and operational discipline. The goal isn’t to predict every threat—it’s to build foundations that can adapt as threats, technologies, and expectations evolve.

Organizations that navigate 2026 successfully tend to:

  • Design identity-first environments
  • Automate where human speed is insufficient
  • Treat resilience as a primary outcome
  • Govern AI and data through enforceable controls
  • Align security architecture with how the business actually operates

If you’re evaluating how these trends apply to your organization—or want help translating them into a practical roadmap, feel free to get in touch.