AI and Cybersecurity: The Opportunities, Risks, and Trends to Watch for in 2026

Keeping your business safe used to be a lot more straightforward. You’d set up a firewall, teach your employees to avoid dodgy links and use stronger passwords, and that was about it. Now, the rise of AI in cybersecurity is creating an increasingly complex environment.

On the one hand, AI and machine learning is introducing new ways for companies to spot and mitigate risks. On the other, it’s giving criminals more sophisticated tools they can use to crack into previously secure systems. Bad actors have AI tools that can mimic your CEO’s voice, launch convincing phishing campaigns, or power thousands of automated attacks per day.

McKinsey even found that phishing accounts have surged by 1200% since 2022 – right around the time when generative AI started gaining steam.

AI is now simultaneously both the source of the biggest risks facing security teams – and potentially our best defence. Since October is cybersecurity awareness month – now’s the perfect time to really dive into the AI security conundrum, looking at the threats, opportunities, and emerging trends.

AI and Cybersecurity: Where Things Stand Today

AI’s link with cybersecurity has grown increasingly more complex in just the last couple of years. Analysts like Gartner have shown that companies are throwing serious money into smarter security – and it’s easy to see why. Yes, AI is risky in its own right, but it’s powerful too.

IBM found that companies using AI-based threat detection have shaved nearly $2.22 million off the average cost of a breach. Plus, these days, we have a lot more tools to experiment with. Not just AI-powered automated threat detection systems, but agentic AI systems that can automate entire security workflows, and machine learning built into networks.

At the same time attackers have gone up a gear. We’re seeing automated malware that changes shape mid-attack, phishing emails that are basically indistinguishable from the real thing, and deepfake video calls that have fooled entire finance departments. One report from Sophos found that AI-fuelled attacks have jumped 76% this year alone.

If criminals are going to be using AI to power attacks, we need to be ready to fight fire with fire, but most businesses are still relying on shaky systems. VPNs that haven’t been patched. Teams who work from cafes without a second thought. A lack of basic visibility into who’s accessing what, and from where. So, what can we really do to balance the scoreboard?

AI and Cybersecurity: How we Can Use these Tools

Most companies are still very much in the “experimental” stage with AI – no matter what they’re planning on using it for. Nobody really knows what to expect, and many are concerned that simply implementing AI into workflows is going to be far too complicated, expensive, or disruptive.

However, there are quite a few ways to get started with AI in cybersecurity that don’t involve tipping everything upside down. Here are just a few examples of the AI security tools and strategies organisations can implement (with just a little help).

1. Spotting Trouble Early (Without Burning Out Your Team)

Most of what teams use AI for boils down to one thing: catching suspicious activity fast.

Let’s say someone in marketing suddenly logs in from a hotel in Brazil at 2:37am, and then starts accessing files they’ve never touched before. That’s not necessarily a hack, but it’s definitely odd. With the right setup, AI can spot that and shout before anyone else even realises something’s off.

Tools from companies like Darktrace or CrowdStrike are constantly looking for changes in behaviour, dodgy traffic spikes, and things that just don’t add up. You still need someone to review what they find, but the legwork is handled automatically.

According to IBM’s 2024 breach report, orgs using this kind of AI shaved off over 100 days in breach detection time. That’s the kind of number you feel in your budget and your stress levels.

2. Responding Before Things Spiral

Once something sketchy is spotted, you need to move. Fast. That’s where AI gets seriously handy. The better tools don’t just raise a flag and walk away. They can isolate a laptop, shut down a session, or even roll back dodgy changes without waiting for sign-off. Almost like having a bouncer at the door who spots fake ID and quietly shows them the exit.

Tools like SentinelOne Singularity do this well. They’re fast, blunt, and, when tuned properly, actually take pressure off your team. You still need a human in the loop, of course, but the key is: you’re not starting from zero every time.

3. Phishing Protection (Because Yep, People Still Click Stuff)

This one’s less about what AI does, and more about what humans keep doing: clicking links they shouldn’t.

Phishing attacks are one of the easiest ways for hackers to gain entry to anything, and they’re so much harder to identify these days. AI tools like Tessian and Proofpoint help by reading between the lines, checking tone, sender behaviour, domain history, and other subtle cues that humans might miss. Basically, they can spot when that “CEO” email isn’t from the CEO.

There are even AI-powered phishing detection tools you can find that come built into existing systems, like Google Gmail or Microsoft Outlook. These tools are particularly valuable now that phishing attacks are skyrocketing. It’s not enough to just “train your staff.” You need filters that can spot scams written better than your own emails.

4. Detecting Deepfakes

If you think deepfakes are still just TikToks of Morgan Freeman reading bedtime stories, think again.

We’ve seen companies lose hundreds of thousands because someone got a phone call from “their CEO” telling them to move funds. The voice sounded right, the mannerisms were perfect, and actually identifying the “scam” was incredibly difficult.

That’s why tools like Pindrop and new security features in Zoom are starting to scan for synthetic audio and video oddities. It’s not foolproof, but it’s better than just hoping your finance team knows your CEO’s voice well enough to say “that doesn’t sound quite right.”

5. Watching the Folks Inside the Walls

Many threats don’t actually come from shadowy hackers. They come from people with access, either by accident or on purpose.

AI-based user behaviour analytics helps keep an eye on this without micromanaging anyone. Tools like Splunk or Securonix build a profile of what’s “normal” for each user. That means if someone in your team suddenly starts acting out of character – accessing files and tools they never use at bizarre times – your system can flag it instantly.

It’s not a matter of implementing 2constant” big-brother style surveillance. about catching stuff before it becomes a full-blown incident. It helps catch problems that would’ve otherwise slipped through every traditional security filter you’ve got.

6. Keeping the Cloud Chaos in Check

Everyone’s working from everywhere now. The number of people logging into systems from an official desk has dropped dramatically. The workplace is now the home, the airport, the local coffee shop – wherever. That means your data lives in more places than ever, and the lines between personal and work devices are blurry at best.

AI helps here by spotting patterns across all those endpoints and platforms. Zscaler, Cisco XDR, and others keep an eye on cloud traffic, flag weird behaviour, and even suggest what to look at first when something feels off.

It doesn’t solve the sprawl, but it gives you visibility, which is the next best thing.

7. Agentic AI: When the Tools Start Thinking for Themselves

This is a slightly newer avenue for AI and cybersecurity in 2025 and 2026: agentic AI. Companies are investing in autonomous systems that can react, plan, test, adapt, and learn.

Picture this: an AI tool notices suspicious login patterns, pulls relevant log files, compares them to recent cases, and recommends a fix. All without anyone asking it to.

Microsoft Security Copilot, for instance, is already able to generate summaries, write detection rules, and dig through logs based on a simple prompt. Others, like Elastic’s AI Assistant, are pushing into real decision-making territory. Obviously, you don’t want to give these things full control, but with oversight? They’re like having an extra analyst available 24/7.

AI Cybersecurity Benefits: Why It’s Worth the Investment

So, why bother exploring all these new use cases? It all boils down to one thing: AI in cybersecurity works. Or at the very least, it works a lot better than what most of us were doing five years ago. AI isn’t a silver bullet for all threats, but it can help tip the scales. AI in cybersecurity:

• Finds the things we miss: Humans are great at a lot of things. Staring at 4 million lines of server logs to spot a weird pattern usually isn’t one of them. AI threat detection works constantly, processing more data than a human can, faster. It’s not perfect. You’ll still get the odd false positive, but it’s far more efficient, and accurate.
• Drives Faster response, less damage: Speed is everything when something goes wrong. The longer an attack goes undetected, the more expensive the fallout. Some systems can even take action without waiting for sign-off. Cut off a suspicious session. Lock down an endpoint. Roll back a dodgy software update.
• Ensures less guessing, more focus: Security alerts come in hot and fast. Some tools throw up thousands a day. Without AI? You’re sifting through them one at a time, hoping the real threat isn’t hiding somewhere in the noise. Modern AI security platforms help triage. They sort alerts by risk, highlight where to look first, and suggest what to do next.
• Reduces human error: Most security breaches don’t happen because the tools didn’t work. They happen because someone clicked a link they shouldn’t have, or reused a password from 2012. AI helps reduce that risk. Email filters are sharper. Risk scoring on accounts is more accurate. Some tools even generate alerts when someone’s about to do something questionable, like emailing a sensitive file to a personal address.
• Improves compliance: If you’ve ever had to deal with an audit, you’ll know it’s not just about keeping the bad guys out. It’s about proving you’ve got systems in place to detect, log, and respond to threats. That’s where AI can help massively. It automates log analysis, tracks access requests, and even produces reports that make audit day slightly less painful.

Perhaps most importantly? AI and cybersecurity systems grow with you. They get smarter over time. They learn what’s normal in your business. They adjust to new workflows, new users, even new apps. This matters if your business is growing, shifting to the cloud, or changing how it works.

The Dark Side: AI and Cybersecurity Threats

For every clever security team using AI to tighten the locks, there’s a cybercriminal using it to pick them faster. That’s the reality now. The same tools we’re adopting to detect threats, automate responses, and stay ahead? They’re being used against us, just with different goals.

Here are the threats you really need to watch out for right now:

1. Deepfake Attacks Are Now a Daily Risk

You’ve probably seen the deepfake Tom Cruise videos online. Impressive. Harmless. But now imagine your CFO getting a call from what looks and sounds like the CEO, asking for an urgent transfer of £75k. Same tone, same face, same mannerisms.

We’re past the era of dodgy Photoshop scams. With generative AI, attackers are producing fake videos, cloned voices, and hyper-realistic chat messages that are fooling even seasoned staff. What used to be an advanced threat is now something almost anyone can do with the right tool and a YouTube tutorial.

2. Smarter Phishing, With No Typos

You remember those old scams with the broken grammar and odd capitalisation? Those don’t happen often anymore. Now, attackers are using language models to craft emails that sound just like your colleagues. Or your vendors. Or your clients. They’re tailored, well-written, and convincing.

The UK’s NCSC reported a sharp spike in phishing attacks powered by generative AI in 2024, with attackers using models like WormGPT and FraudGPT to create personalised scams that bypass filters and fool even experienced users.

3. Attackers Are Using AI to Write Better Malware

AI is great at coding too. Cybercriminals are feeding code snippets into large language models and getting back malware that’s more efficient, more evasive, and harder to detect. Some of these models can even tweak the code automatically if it doesn’t work first time. So, instead of attackers spending weeks testing and refining malware, they’re doing it in hours.

This kind of automation is pushing the volume and sophistication of attacks way up. Even low-skill hackers, people who would have been irrelevant five years ago, can now launch serious breaches using off-the-shelf tools.

4. Shadow IT Is Getting Worse

With AI tools like ChatGPT or Midjourney just a click away, staff are increasingly using unsanctioned apps at work. Sometimes for convenience. Sometimes because IT is too slow. Either way, it’s creating a minefield of shadow IT, devices and apps your security team has zero visibility on.

The trouble is, these tools often connect to company data. So when something goes wrong (and it will), the breach happens outside your security perimeter, and you won’t know until it’s too late. Add in the fact that some AI tools retain input data for training or have unclear privacy policies, and suddenly you’ve got a compliance issue too.

5. Data Poisoning and Model Hacking

One of the newer AI cybersecurity threats we’re seeing is data poisoning, where attackers deliberately feed bad data into your models to mess with their outputs.

Imagine an AI-powered fraud detection system that slowly starts allowing more dodgy transactions through because it’s been “trained” on manipulated patterns. It’s subtle. It’s long-game. And it’s incredibly hard to spot without regular audits.

There’s also prompt injection, where attackers hijack how AI systems interpret commands. It’s happening more often, especially with chatbots and customer-facing systems.

Ethical Gaps and Governance Grey Areas

Even when attackers aren’t directly involved, the way companies use AI can backfire. If you’re training models on customer data without proper governance? That’s a GDPR problem. If your AI tool flags a staff member as a threat based on flawed behavioural data? That’s an HR nightmare waiting to happen.

As more systems rely on automated decisions, the ethical implications get harder to ignore. Especially if there’s no human review or override built in. The EU AI Act is just one example of incoming legislation that will force companies to take governance more seriously. The reality is, most businesses are still figuring this out as they go.

AI and Cybersecurity: How to Dive In

Alright, so we’ve covered the good and the bad. You know AI security can help protect your business, but you also know it can go sideways if you rush in blindly. That’s where this bit comes in.

If you want to bring AI into your cybersecurity stack the right way, it’s all about balance, structure, and a bit of healthy paranoia.

1. Start With a Proper Risk Assessment

Before you sign off on any flashy new tools, pause. Get your house in order first. Do an audit of your existing setup. What threats are you actually facing? Where are the gaps? Is it phishing? Endpoint chaos? Too many alerts, not enough time?

Figure out what needs solving before you go adding more complexity. We’ve seen businesses buy expensive AI platforms that sit unused because nobody knew what they were for.

2. Get the Basics in Place First

AI won’t fix a broken foundation. If your patching is slow, your access controls are a mess, or you still don’t have multi-factor authentication rolled out, AI isn’t the answer on its own.

Get the fundamentals sorted. Then use AI to improve what’s already working. Think of it like building muscle. You don’t start with advanced weightlifting techniques if you haven’t learned how to stand up straight.

3. Build Governance Around Your Tools

AI doesn’t just bring in new tech, it brings in new risks. Privacy. Bias. Misuse. Bad decisions made at machine speed. Before you start plugging in any tool, decide how you’ll manage it. Who owns it? Who checks the outputs? What data are you feeding it? How often is it being reviewed?

It’s not just about security, either. Under the EU AI Act, you’ll need explainability and audit trails for automated systems, especially in sensitive areas like finance, healthcare, or employment. Treat AI like any other high-risk system. Set guardrails. Define roles. Bake governance in from day one.

4. Train People, Not Just Systems

Most breaches still come down to one thing: people. Doesn’t matter how advanced your AI is, if Dave in marketing uploads payroll data to a dodgy AI chatbot, you’re still in trouble.

So yes, invest in detection tools, but also invest in education. Run awareness campaigns. Set up phishing simulations. Teach your teams what deepfake fraud looks like. Let them know the signs, the scams, the slip-ups to avoid.

5. Roll It Out in Phases

Don’t try to transform your entire security setup overnight. It’s a recipe for chaos. Start with one clear use case. Maybe it’s email protection. Maybe it’s smarter SIEM alerts. Maybe it’s user behaviour analytics. Nail that, then move on to the next.

This phased approach gives you room to learn, adjust, and prove the value of AI before you go all-in. Remember, if your team hates the new tools, they’ll ignore them. Gather feedback regularly and make sure you’re ready to adapt.

6. Vet Vendors Carefully

There are a lot of cybersecurity vendors claiming to offer AI. Some of them actually do. Others? They just slap “AI-powered” on the box and call it a day.

When choosing tools, ask questions. How is the model trained? What happens with your data? Can you audit the decisions it makes? How easy is it to integrate with your current stack? Check reviews. Ask for demos. And if you’re unsure, use an independent advisor to help compare the options. TechGrants can help you make the right choice if you’re not sure where to start.

What’s Coming Next: AI Cybersecurity Trends to Watch in 2025

Right now, still in cybersecurity is basically the same as moving backwards. The threat landscape doesn’t wait for anyone, and neither does the tech trying to keep up with it.

We’re at a point now where AI and cybersecurity are no longer separate conversations. They’re intertwined, on both sides of the fence. Your attackers are using it. Your vendors are building it into their tools. Your team is (hopefully) learning how to work with it, not just around it.

If you’re not building AI into your defences now, you’ll be racing to catch up later, probably in the middle of an incident you didn’t see coming.

So where do you go from here?

Run an audit, find your weak points, and connect with a partner that can help.

At TechGrants, we help businesses like yours find the right vendors, access available funding, and build security strategies that actually work. Whether you’re looking for AI tools to boost your threat detection, or you need help understanding what “Zero Trust” looks like in real life, we’ll help you sort the real solutions from the noise.

Ready to upgrade? Contact TechGrants today.