The past year was dominated by one theme: scale. Scale in data, in AI adoption, in the speed of attacks, and in the number of systems security teams must protect without additional resources.
In 2025, organizations tried to understand how deeply AI systems touch their environments, how much of their data is unnecessarily exposed, and where risk hides in third-party software and vendors. At the same time, attackers quietly shifted to automation, using AI tools to increase impact while reducing manual effort. Supply chain compromises cascaded through dozens of organizations, credential theft became the most common breach vector, ransomware moved almost entirely to extortion models, and generative AI moved from experimental to production, creating new attack surfaces we are still learning to secure.
2026 won’t be a clean break. It will be the year these trends mature, intersect, and force long-term changes in how we secure data, identities, and systems. Here’s how the landscape looks from where I sit.
Traditional ransomware (encryption followed by negotiation) continues to decline. Attackers have learned that stealing data and threatening to publish it is faster, cheaper, and more profitable. More than 80% of ransomware incidents now involve exfiltration, and that number will approach universality in 2026.
Three shifts will define the year:
- Data theft becomes the primary lever.
Encryption is optional; exposure risk drives payment. Stolen customer records, financial data, intellectual property, and internal communications create pressure faster than system downtime. - Supply chain attacks multiply the damage.
Compromising a single vendor, MSP, or software provider can impact dozens of downstream organizations. A provider’s breach becomes your breach, regardless of your internal controls. - Boards become directly involved.
When a supplier’s compromise can halt operations across business units, ransomware becomes a business continuity problem, not just an IT event.
As a result, enterprises must treat third-party access, developer environments, and shared data repositories with the same rigor as production systems. The perimeter now extends to every partner that touches your data.
The biggest change in threat activity will be the rise of autonomous AI agents controlled by attackers.
These agents can generate phishing at scale, probe networks, and mutate malware faster than defenders can analyze it, using tools already publicly available. Early frameworks show AI chaining reconnaissance tools, scanners, and exploitation modules with minimal oversight.
The economics shift when the cost per attack approaches zero. Attacks that once required skilled operators can now be executed by self-iterating systems that learn from failure and operate in parallel across hundreds of targets.
Defenders will have to respond in kind. AI-driven agents will assist in triage, anomaly investigation, alert validation, and even initial containment. Human analysts will supervise these systems rather than manually reviewing every alert. The challenge becomes ensuring defensive agents do not introduce new vulnerabilities. Prompt injection, misuse of tools, and privilege escalation risks all apply to AI systems just as they do to humans.
2026 will be the first year where a meaningful share of both attacks and defenses areconducted machine-to-machine.
AI assistants and LLM-powered tools are now embedded across business workflows, raising a central security question: What exactly is the AI seeing, storing, and sharing?
Most organizations still treat AI systems as application functions rather than as actors requiring authentication, authorization, and audit trails. When an AI assistant ingests documents, it is accessing data. When an agent makes an API call, it is performing an identity operation. These interactions must be governed.
In 2026, organizations will formalize AI data access governance driven by both compliance requirements and the need to prevent silent data leakage. This includes:
- Defining AI systems as non-human identities with clear access boundaries
- Monitoring the data each model or agent interacts with
- Blocking AI tools from ingesting sensitive information unless explicitly permitted
- Recording AI interactions for accountability and forensics
Without these controls, enterprises risk exposing sensitive information, violating data residency obligations, and creating audit gaps. AI governance will become as fundamental as identity management.
Enterprises have accumulated vast amounts of data, much of it unused but still accessible, creating a massive attack surface. With AI systems able to query and traverse environments far faster than humans, overexposed data has become an operational liability.
In 2026, the focus shifts from reacting to breaches to reducing what attackers could reach in the first place.
Key elements of this shift include:
- Using access-pattern analytics to identify dormant data
- Expiring unnecessary permissions automatically
- Reducing oversized shared repositories
- Enforcing stricter controls around AI systems that can inadvertently widen access
The mindset moves from “detect when something goes wrong” to “shrink the blast radius before anything happens.” Reducing the exposed data footprint will meaningfully limit incident severity even as attacks grow.
Quantum computing is not a 2026 threat for most organizations, but preparing for it is. Two forces create urgency:
- Harvest-now-decrypt-later attacks.
Threat actors already steal encrypted data with the expectation that future quantum computers will decrypt it. Long-retention data (financial, medical, intellectual property, government) is at risk. - Migration will take years.
Replacing cryptographic libraries and updating protocols across legacy systems is a long, complex effort. With NIST’s post-quantum standards published in 2024 (ML-KEM, ML-DSA, SLH-DSA), regulated sectors will accelerate adoption.
Organizations with sensitive long-life data must begin crypto-agility planning now: inventorying cryptographic dependencies, identifying vulnerable algorithms, and designing frameworks to swap primitives without major rewrites. By the time a cryptographically relevant quantum computer exists, the window for protection will have closed.
2026 marks a shift from reactive security to structural realignment. AI systems now participate in both offense and defense. Data volumes exceed what manual controls can manage. Supply chain complexity outpaces traditional risk assessments. And long-term threats like quantum computing demand preparation now.
Security leaders should ask:
- Can we enumerate every AI system accessing our data, and do we know its permissions?
- What percentage of our sensitive data hasn’t been accessed recently, and why is it still reachable?
- If a major supplier were compromised tomorrow, how quickly could we identify and isolate the impact?
The answers will determine readiness for what’s coming in 2026.