AI-Powered Cybersecurity in 2026: What Experts Say

We are seeing unprecedented adoption of LLM and ML models in both compliance and cybersecurity in the last 18 months, even among larger mid-market firms in traditional industries.

Compliance is heavily tied to regulations. And this is well-documented, a byproduct of thousands of meeting memos, acts, guidelines, laws coming together. Language models are uniquely designed to process and interpret data with high level of predictability and output efficiency in a matter of minutes.

Security is traditionally led by a mix of data, texts, and repetitions. Data for compiling servers, websites, pages, systems. Text for processing regular expressions, cross-site script injections, blind SQL injections, or other transformations. And repetitions to brute force all permutations effectively (and in stealth).

We always employ sufficient human capitals in guiding these initiatives, verifying game plans before execution, and assessing output reports to filter out noise. But efficiency gains are clear, and the mid-market and private equity world has been adapting successfully.

At CallCrewAI, our AI agents handle calls, emails, job bookings, and invoice chasing for trades businesses. Every call is a regulated event. The compliance question isn’t “is our cloud secure?”, it’s “can we prove what an autonomous system said to a customer at 11pm on a Tuesday, and that the customer consented to talking to an AI?” That shift from securing data to attesting to agent behaviour is where AI is forcing compliance to evolve.

First lesson: determinism is a compliance feature, not just an engineering one. LLMs are probabilistic by default, but in regulated workflows every action must map to a structured tool call with logged inputs, outputs, and a decision trace. “The AI decided” isn’t an audit answer. Consent has to be machine-handled in the first three seconds: recording disclosure, AI disclosure, opt-outs. All enforced by the agent itself.

Second: AI is now both the threat and the defence. We’re seeing voice cloning attacks against the trades businesses we serve, with fraudsters impersonating customers to redirect invoices. The same speech models that power our agent are what we use to detect synthetic audio inbound. The line between “AI vendor” and “security vendor” is collapsing.

What’s underrated is that, done right, AI raises the compliance floor for industries that previously had none. For an SMB trades business, every interaction is automatically logged, transcribed, and searchable. This was never true when a human receptionist took the call. The winners won’t be the ones bolting AI onto existing GRC tools. They’ll be the ones treating every autonomous action as an auditable event from day one.

AI is changing cybersecurity in a much deeper way than simple productivity gains. It is changing how attacks are built, how fast they move and how security teams need to operate in response. We are already seeing AI-powered phishing surge, deepfakes scale rapidly and breakout times measured in minutes. At the same time, SOC teams are still dealing with thousands of alerts per day, more than 80% false-positive rates and almost 40% alerts left uninvestigated, as studies say. That gap between attacker speed and defender workflow is becoming one of the defining pressures in cybersecurity today.

This is why I believe the real AI shift in cybersecurity is not about adding another assistant on top of existing tools. It is about moving from alert handling to security reasoning. The next generation of security operations needs to correlate signals across the full attack chain, build context from identity, endpoint, network and threat intelligence, and reduce the manual burden on analysts, but keeping human in-the-loop. In that sense, Agentic Investigations are important because they represent a new operating model, not just a new feature.”

“The platforms that will matter most are the ones designed for this shift. In our case, that thinking has shaped CYBERQUEST toward unified visibility, behavioral analytics, AI-assisted investigation and automation built into the investigation flow rather than added at the edges. More broadly, I think this is where cybersecurity is heading: less noise, more context, faster investigation and a much tighter link between detection, reasoning and response. AI is not just accelerating cybersecurity. It is redefining what effective cybersecurity operations should look like.

AI Is Redefining Product Security, Compliance, and Control

Over the past decade, product security has shifted left, becoming embedded in the SDLC. In 2025, AI accelerated this trend by scaling code generation, testing, and vulnerability detection. However, compliance has remained largely periodic and disconnected from how software is built and operated.

In 2026, that model will break. Organizations need to move from point-in-time assurance to continuous, AI-driven risk management embedded across the product lifecycle. Product security and compliance are no longer separate concerns; they are integral to how modern products are designed, built, and delivered.

Structurally, product security, compliance, and engineering are converging. AI is becoming part of the control plane, enforcing policies and validating controls from code to runtime, reducing reliance on manual processes and fragmented tooling.

The most significant shift is in signals. Instead of relying on static evidence, organizations will prioritize real-time telemetry, behavioral data, and continuous validation to assess and manage risk.

By the end of 2026, product security and compliance will operate as a unified, signal-driven system, where strategy, structure, and signals remain continuously aligned through AI.

In my opinion, the paradigm is shifting from whether an organization has all the compliance documentation it needs to whether it can prove, in real time, who approved access, who completed training, and what changed.

Used badly, AI will only worsen current problems. If it’s mostly applied to generating more policies, records, and internal docs, it’ll flood already overloaded systems with even more data, much of it not particularly useful.

I’d say the better use of AI is not more paperwork, but better proof.

I expect trusted digital credentials to become more important in compliance and cybersecurity, as they replace claims and assertions with instantly verifiable evidence.

For years, compliance was nothing more than a documentation exercise. You wrote the policy, ran the training, stored the record, and then made sure you’re prepared for an audit. AI is evolving that paradigm towards continuous proof.

The practical advantage is visibility. Organizations can move closer to a system where they know who completed training, who approved access, what controls changed, and whether the evidence they have is current with minimal friction.

And, as many in the industry already intuit, regulatory pressure will increase here, which means continuous proof of trust will be that much more important in terms of giving companies an edge over the ones stuck in the documenting periodically phase.

What are the most used keywords associated with artificial intelligence?

Innovation, opportunity, cutting-edge technology, competitive advantage.

These are the terms that dominate the conversation and shape how organizations focus on AI: as a key factor for growth, speed, and differentiation. But it’s worth asking a more practical question: is artificial intelligence just a tool, or something that fundamentally changes how we operate?

Today, I no longer see artificial intelligence as just another tool we deploy.

It is fundamentally changing how I think about our core business focus: cybersecurity and compliance. At Ventivo, we are moving beyond old-fashioned, checkbox-style frameworks and questionnaire-based back-and-forth approaches that were designed for static environments.

Those approaches struggle to keep up with systems that evolve continuously and make decisions in real time. Instead, we are moving toward more flexible models systems that learn from new data, adapt in real-time, and respond as threats arise. This shift is not just about efficiency; it is about maintaining visibility and control in environments where change happens faster than traditional processes can handle.

From my perspective, AI operates as a double-edged sword. On one hand, it is a powerful force multiplier. It helps us identify patterns earlier, flag anomalies faster, and reduce the time between detection and response critical advantages in cybersecurity, where minutes can make the difference. At the same time, these capabilities are not exclusive to defenders.

Malicious actors now have access to the same acceleration: they can automate reconnaissance, refine attack strategies more quickly, and exploit vulnerabilities with greater precision. What once required time and manual effort can now happen at scale and at speed. This dual impact is what makes AI fundamentally different from previous waves of technology.

Working alongside AI allows teams to operate at a scale that would have been unrealistic just a few years ago.
But that same scale also amplifies risk because decisions are made faster, systems are more complex, and the margin for error becomes smaller. This is where the real concern begins.

The frameworks we have relied on for years were not designed for autonomous or semi-autonomous systems making split-second decisions. They assume predictability, human-paced processes, and clear checkpoints assumptions that no longer hold.

Without adaptation, these frameworks create blind spots: areas where decisions are made without sufficient oversight, accountability becomes unclear, and risks accumulate before they are even visible.

At the same time, a critical gap is emerging one that is becoming increasingly difficult to ignore at the industry level: organizations are adopting AI far faster than they are learning how to govern it.

This gap is not theoretical. It translates directly into exposure of security vulnerabilities, compliance failures, and operational risks that are harder to detect and even harder to control. Governance, therefore, is no longer a supporting function; it is a central requirement. It must evolve to provide continuous oversight, clearer accountability, and controls tailored to AI-driven environments. Ultimately, that gap between adoption and governance is where risk concentrates. To end on a constructive note, the path forward is clear.

We must treat AI not just as a source of innovation but as a serious risk domain that requires the same level of discipline, rigor, and continuous attention as any critical business function. Organizations that recognize this early will not only move faster they will move more securely and with greater confidence.

The consensus position on AI in tax compliance right now is that it should be assistive. Humans in the loop. Explainability over automation. Augmentation, not replacement. Start narrow, earn trust, expand scope later.

It is a defensible position if you are writing a white paper. It is the wrong architecture if you are actually building the system.

Start with the customer. The 2018 Wayfair decision turned sales tax into a nexus problem for every online seller in the United States. Roughly 13,000 taxing jurisdictions, each with its own rates, taxability rules, sourcing logic, and filing cadences. The population of businesses suddenly in scope is overwhelmingly small and mid-market. Shopify brands, SaaS startups, service businesses doing multi-state revenue. Most of them do not have an in-house tax team. The entire “assistive copilot” frame assumes a tax professional sitting at the other end of the interaction. That assumption does not hold for the majority of the market.

An assistive tool for a customer with no tax staff is a faster spreadsheet. The workload does not move. What moves the workload is a system that executes the full loop without a human approving each transition: ingest transactional data from the customer’s stack, monitor nexus exposure in real time, register the business where thresholds are crossed, compute the right rate at the right SKU at the right address, file on the right cadence, and remit on time.

The objection from incumbents is that this cannot be a black box, because a regulator will eventually ask how a number was produced. That objection conflates explainability with human mediation. They are not the same property. A well-architected agent produces a complete audit trail by construction: every input, every rule version, every source citation, every model output, every deterministic check, every reconciliation against the customer’s source-of-truth systems, all timestamped and queryable. That is a more rigorous artifact than an analyst clicking through a configuration UI and hoping the rationale is still in their head two years later. Autonomy is not the enemy of auditability. Poor architecture is.

The real engineering bar is not “humans or agents.” It is how you compose probabilistic and deterministic components in a system where the cost of a wrong answer is a regulator notice. Modern language models are strong at the probabilistic parts, specifically product taxability classification and rule interpretation, which historically required a specialist reading a product catalog. Grounding those outputs in primary sources (statutes, admin codes, department of revenue letter rulings), routing them through deterministic tax engines, and reconciling against the customer’s ERP, storefront, and bank is the actual work. Evals at each layer. Confidence thresholds that route edge cases to a human reviewer. Closed-loop feedback on every filing outcome. That architecture is what lets the system act on the customer’s behalf, not just suggest what they should do.

That shift in responsibility is the part of the category most vendors have not fully absorbed. In assistive AI, the customer owns every output end to end. In agentic AI, the system carries a much larger share of the operational weight. That is a different product and a different business model from a copilot, and it is not something a legacy rule engine retrofits without rewriting its core.

My prediction: the category separates cleanly in the next two years. Assistive copilots sold into existing enterprise tax teams that want faster analysts. Operational agents sold into the much larger market that never had an analyst to begin with. Both will exist. Only one is a new architecture.Sales tax is the first compliance domain where this split becomes obvious. It will not be the last.

There are two angles worth separating here: compliance, and cybersecurity more broadly.

Let’s start with compliance. It’s a complicated topic mostly because there are so many frameworks and standards that companies can (or must) follow to improve their security posture. They’re intentionally written broadly so they can apply to any infrastructure or environment, and that’s also the catch. Because the controls aren’t very specific, it’s easy to get confused about what a given control actually requires. This is where AI comes in. If you feed an AI with context about how your infrastructure works, it can make resolving compliance controls much easier. It cuts down the time GRC teams have to spend back and forth with infra and security teams just to figure out what’s needed to meet a control.

On the broader cybersecurity side, I think AI shines in two areas: incident triaging and vulnerability finding. There are tons of tools on the market for every type of security need, and security analysts don’t have an easy life trying to correlate incidents when they happen. But if an AI has all the data fed into it, it can correlate events very easily and explain to SOC analysts what happened and what needs to be done. The only thing left for the analyst is to take action.

For the vulnerability side, the recent Claude Mythos preview is proof: frontier models are now finding real vulnerabilities at scale. That’s great, until it lands in the wrong hands. But on the flip side, AI can also help remediate those same vulnerabilities.

All in all, AI is accelerating progress across the board, from compliance interpretation, to incident response, to vulnerability discovery and remediation.