Prompt Security’s AI & Security Predictions for 2026

We’ll be honest: predicting anything in AI is like trying to nail Jell-O to a wall.

But we’ve done this long enough to know two things:

Some predictions will age well.

Some will age like an overripe avocado.

Either way, the tradition continues.

And before we jump into what 2026 has in store, don’t miss the recap video on last year’s hits and misses, proof that even security folks occasionally get to say “told you so.”

What’s clear heading into 2026 is that AI is no longer an experiment sitting in someone’s innovation lab. It’s embedded in workflows, powering decisions, and quietly rewriting the boundaries of what organizations can, and must, secure. The stakes are higher, the systems are more autonomous, and the threat landscape is evolving faster than traditional models can keep up.

So what do we think 2026 has in store for us in the AI & Security space?

Agentic AI: Expanding Both Capabilities and Risk

Despite the hype, agentic AI, systems that do not just generate answers but take actions, is still early in real enterprise adoption. Gartner’s latest CIO survey puts this into perspective. While 52 percent of enterprises have already adopted traditional AI and 58 percent have adopted GenAI, only 17 percent report adopting agentic AI today. This gap shows that most organizations are still watching from the sidelines.

These agents will start showing up in data processing, IT operations, finance tasks, customer support, and internal decision flows.

Last year, we predicted agentic AI would be deeply integrated into day-to-day operations. And while we’ve seen meaningful progress, especially for routine tasks enabled by the Model Context Protocol (MCP), adoption at scale isn’t here yet. Most organizations are still experimenting, mostly because the operational readiness just isn’t there. They need training, governance, guardrails, and a much clearer understanding of what safe autonomy even looks like.

That said, 2026 is poised to shift this trajectory. As tooling stabilizes and governance frameworks mature, agentic AI will move from scattered pilots to structured deployments that actually deliver business value.

The upside is real: scalable automation, faster processes, fewer repetitive tasks, and,  in security, agents that can detect threats, respond to incidents, and patch vulnerabilities faster than human teams.

But autonomy cuts both ways. Misconfiguration, drift, or compromise can introduce entirely new attack pathways. Agents can magnify impact, make mistakes at machine speed, or take actions no one realized they had permission to execute.

And this isn’t just theory. According to a recent article from Axios, suspected state-backed attackers used autonomous AI tools to conduct cyberattacks, a real-world example that shows the dangers of powerful AI agents falling into the wrong hands.

Organizations will need strict governance frameworks around identity, permissions, audit trails, and behavior monitoring to keep agentic systems safe, predictable, and accountable.

AI in Critical Industries: The Healthcare Imperative

AI in Critical Industries: The Healthcare Imperative

Healthcare has always been a high-value target, but the combination of aging infrastructure, nonstop operations, and extremely sensitive data makes the sector uniquely exposed. Attackers know it, and 2026 won’t be any kinder.

We expect AI to sit even more firmly at the center of healthcare cybersecurity. AI-driven anomaly detection, triage assistance, and pattern recognition will become standard across clinical and administrative systems. These tools can process volumes of data humans simply can’t, and they’ll surface threats earlier and with far more context.

In 2025, multiple research teams demonstrated that healthcare models are highly susceptible to data poisoning, in some cases requiring as few as 100-500 poisoned samples to sway diagnostic outputs across different institutions. For an industry that depends on high-integrity data, this is a serious warning shot.

But let’s be clear: AI isn’t replacing clinical judgment or security expertise anytime soon. It doesn’t understand care protocols, patient safety boundaries, or hospital operational nuance. The healthcare organizations that win will be those that pair AI's speed with human oversight that knows when something looks wrong, not just when the model says it is.

Companies that lean too heavily on automation without grounding it in expert review will struggle against the next generation of AI-enhanced threats.

The Evolution of Cyber Threats in the Age of Autonomous AI

AI-driven cyberattacks have been a topic of discussion for years, but in 2026 they will become a defining reality. We expect to see the first wave of fully autonomous intrusion attempts that require little to no human oversight from attackers. These AI agents will be capable of performing reconnaissance, exploiting vulnerabilities, escalating privileges, and exfiltrating data at a pace no traditional security tool is prepared for.

We’re already seeing early signs of this shift. In late 2025, Anthropic disclosed that a state-backed threat actor had manipulated its model, Claude Code, to conduct an AI-orchestrated espionage campaign across more than 30 organizations. The AI system reportedly handled the majority of the intrusion steps autonomously, from reconnaissance to exploit development and credential harvesting, highlighting how quickly adversaries are operationalizing AI for real-world attacks.

As Itamar Golan, CEO and co-founder of Prompt Security, notes:
“We’ll see fully autonomous, AI-driven cyberattacks become the new norm. Adversaries can now automate the majority of an intrusion with almost no human expertise. Companies that don’t adopt automated, AI-powered defenses will find themselves outpaced by threats that evolve faster than any traditional security model can keep up.”

This shift in the threat landscape will force organizations to treat non-human identities, AI agents, and automated workflows as first-class citizens in security programs. Identity, access, and behavior monitoring will need to extend to machines and autonomous systems, not just people.

The Emerging Threat of Data Poisoning

With more enterprises training and fine-tuning models, data poisoning is set to move from an “interesting security talk-track” to something the industry actually needs to take seriously. Poisoned data has a way of slipping into the mix quietly and reshaping how a model behaves, nudging outputs in directions no one intended. It might introduce bias, hide the signals you care about, or shift decisions ever so slightly until they are nowhere near where they should be.

The challenge is that poisoned data rarely announces itself. It looks like everything else. By the time anyone realizes something is off, the model has already internalized the behavior and carried on as if nothing happened.

This is why the resilience of the underlying systems matters. Third-party model providers will need stronger checks on the data that shapes their models, along with tighter provenance and better evaluation methods that help catch subtle manipulation before it becomes part of the model’s worldview. With so many enterprises building on top of these systems, keeping the data trustworthy becomes part of the shared responsibility of modern AI adoption.

Enterprises also have a role as they feed models more of their own content, including documents, workflows, and customer interactions. Understanding what is influencing the model and where that data originates will matter more as AI becomes woven into everyday operations.

In 2026, data poisoning is not about dramatic failures. It is about keeping the inputs clean enough that the outputs stay predictable, useful, and aligned with what the business actually needs.

Advances Toward AGI: Progress, Pressure, and Practical Realities

Every year someone asks, “Is this the year of AGI?”

And every year the answer is… not quite.

2026 will not deliver fully general intelligence. However, it will deliver models that behave far more independently than anything we have used before. They will reason across tasks, take initiative, and operate with context that increasingly resembles human-level decision flow.

That progress brings pressure. Not the science-fiction kind. The governance kind.

As models become more capable, the risks tied to autonomy, misalignment, and unintended behavior also grow. We already saw early versions of this in 2025, when advanced models created multi-step plans, self-corrected errors, and pursued goals across long-running sessions. In an enterprise setting, this can lead to unexpected outcomes. For example, an AI assistant might autonomously escalate access, rewrite internal documentation, or trigger workflows because it interprets a vague instruction as authorization for broader action.

These are not “rogue AI” stories. They are signs of increasing independence and evidence of how even almost-AGI systems can complicate governance, auditability and operational control.

So is 2026 the year of AGI? Probably not.

But it is the year enterprises begin acting as if AGI, or something close enough to complicate things, could be on the horizon.

And that alone will reshape how organizations think about model responsibility, safety and control.

We’ll revisit these predictions in 12 months to see what the industry proved right, wrong, or completely unexpected. Here’s to a steady, secure, and well-governed 2026.