How AI is changing cybersecurity: 8 takeaways from our expert webinar

AI security right now isn’t a question of ambition or intent anymore. It’s a question of exposure.

Most organizations didn’t consciously adopt AI in security-critical areas. Instead, it landed silently in platforms, layered into workflows, and reshaped decisions before governance had a chance to catch their breath.

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Security teams are now accountable for systems that reason probabilistically, act at machine speed, and don’t always behave the same way twice. That alone should change how readiness is measured.

TL;DR: AI Security in 2026

• AI security today is about exposure, not experimentation.

• Generative and agentic AI systems are shifting from assistive tools to autonomous decision-makers.

• Attackers are using AI to scale reconnaissance, phishing, and adaptive intrusion at machine speed.

• Fully autonomous SOCs remain unrealistic because of the non-deterministic nature of LLMs.

• Human validation and oversight are still essential in AI-assisted security operations.

• AI investment is increasing, but failures often stem from unclear ownership and weak governance.

• Security roles are evolving toward AI validation, red teaming, and behavioral testing.

• Organizations that test AI systems in controlled environments reduce long-term risk.

1. AI is moving from handy helper to decision maker

Security teams have been working with machine learning for years. It was narrow in scope and largely invisible in day-to-day operations. Spam filtering, anomaly detection, risk scoring. Useful, contained, predictable enough.

Until Generative AI comes along to bust into that containment.

The shift is less about marginal gains in accuracy, and more a commitment to agency. AI systems are now capable of chaining actions, navigating unknown environments, and operating beyond single prompts or queries. They don’t just surface information, but they participate in execution too.

This is an essentially foundational technology shift, even more transformative than cloud. Cloud adoption forced organisations to rethink infrastructure ownership. AI pushes them to confront decision ownership, all too often without clear answers about where responsibility should sit. And security has always been sensitive to that problem.

2. Attackers are working smarter AND faster than ever

The most consequential change on the threat side isn’t creativity or technical novelty. These days, it’s all about consistency.

AI allows attackers to strike faster and smarter—without hesitation

AI opens the window for attackers to operate competently at scale without the constraints that usually slow human-led activity.

Social engineering no longer relies on crafting one single convincing message. Personas can be tweaked on the fly; language and tone can shift mid-conversation. Voice cloning deletes an entire layer of friction from impersonation as we know it.

To that end, reconnaissance needs to be an always-on situation. AI agents can enumerate and correlate continuously, adjusting targets faster than patch cycles or asset inventories can realistically keep up.

Once they’ve slipped into your environment, attackers aren’t navigating on pure vibes. AI assistance can interpret telemetry, parse configurations, and adapt behaviour while an operation is still unfolding. 

And it’s worth remembering: most defensive models were built around pauses in attacker activity, and those are becoming more noticeable by their absence in more recent times.

3. Fully autonomous security isn’t here

Claims around fully autonomous SOCs and self-healing security systems are everywhere. The gap between the promise and the operational reality remains wide.

The limitation isn’t a lack of ambition. It’s a property of the technology itself.

LLMs are non-deterministic. Identical inputs can produce different outputs depending on context, internal state, and probabilistic weighting. That flexibility is part of their value, but it introduces risk in environments where consistency matters.

Every time you give an LLM the same input, it can produce a different output.

That variability is manageable when AI supports human decision-making, but it becomes a real risk when AI is expected to replace it completely. 

Today, AI earns its place by accelerating triage, enriching context, and reducing the time analysts spend on repetitive tasks. When it starts executing actions without verification, small errors stop being isolated and start compounding.

4. Investment in AI keeps climbing (and it probably won’t stop for now)

Security leaders are all too aware of these limitations, so spending on AI continues because the alternative is trying to scale human effort against machine-speed threats.

A 2025 study by MIT, for example, found that 95% of corporate GenAI failed to deliver returns. The issue wasn’t actually model capability—in the majority of cases, the technology worked as expected. The failures came from:

• Unclear ownership

• Brittle data

• Asking teams to adapt to new tools without changes to workflows or decision rights.

AI adoption does have a habit of exposing organisational weaknesses that were already there. It just does it a fair bit faster and with less room to hide.

5. Security security roles are already evolving

AI isn’t cutting people from security teams, but rather changing where effort is applied.

Red teaming, in particular, can no longer rely on assumptions of deterministic behaviour. AI systems respond differently depending on context, interaction history, and data exposure. Testing them requires probing behaviour rather than triggering known failure states.

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Traditional exploitation is static. AI exploitation is probabilistic.

On the defensive side, SOC work is moving away from execution and toward validation. As AI absorbs repetitive tasks, analysts spend more time interpreting outputs, checking reasoning, and deciding when not to act.

This introduces a different kind of risk. Analysts who blindly trust AI will make expensive errors.

The problem isn’t that AI will always be wrong. It’s that confidence can become misplaced very quickly when systems appear authoritative.

6. Knowing the AI basics is better than specializing right now

Not every security professional needs to understand model architecture in pain-staking depth. Every security professional now works alongside AI systems in some capacity.

That requires a baseline ability to recognise common failure modes, question outputs that don’t align with context, and understand how prompts, data, and environment shape behaviour.

Teams that treat AI as an oracle tend to discover problems late. Teams that treat it as a fallible collaborator tend to catch issues earlier, when they’re still manageable.

7. Silos can’t contain AI

AI isn’t something that slots neatly into existing silos.

Treating AI security as a separate initiative owned by a single function almost guarantees blind spots. Red teams need to understand how defensive AI systems behave. Blue teams need to anticipate AI-assisted attack paths. Testing needs to feed into real workflows rather than living in parallel programmes.

Siloed models struggled during cloud adoption. AI places even more strain on them.

8. Start small and test often

Organizations making progress aren’t sprinting toward full automation. The smartest ones are creating controlled exposure.

That usually involves testing AI systems with the same discipline applied to infrastructure, running limited experiments with AI-assisted workflows, and paying close attention to how humans and models perform together under pressure.

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The emphasis isn’t on novelty. It’s on understanding where assumptions break down; rolling out AI without crystal-clear control mechanisms pushes risk forward rather than removing it.

Final thoughts: What’s becoming clear when it comes to AI and cybersecurity 

At this stage in the game, AI-related security failures aren’t exactly shocking. They’ll all be explainable in hindsight.

The organizations that struggle won’t just be the ones that avoided AI entirely. They’ll also be the ones that adopted it without changing how decisions are reviewed, challenged, and owned.

Augmenting your organization with AI increases capability. It also increases the blast radius when something goes wrong. And security teams don’t get to opt out of that trade-off.

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