The Impact of AI on Cybersecurity [How It Helps & Where It Hurts]

CYBERSECURITY

The Impact of AI on Cybersecurity [How It Helps & Where It Hurts]

AI has entered the cybersecurity chat, and it didn’t come empty-handed. It’s making defenders faster, sure. But it’s also giving attackers new tricks: better phishing, faster break-ins, and smarter evasion. The same tech that helps you spot threats can also help your adversary craft them.

So what’s the real impact of AI on cybersecurity? Short version: it’s a double-edged sword.

In this post, we’ll dig into how AI is helping security teams stay sharp — and how it’s also blowing holes in old defenses. We’ll hit real examples, sketchy trends, and the biggest mistakes orgs are still making.

Let’s get into it.

Table of Contents

• AI as a Powerful Ally in Cybersecurity Defense

• AI-powered Cyber Attacks & Other Risks

• A Widening Attack Surface — And Shrinking Response Time

• What Organizations Are Getting Wrong About AI Security

• Tips for Securing AI Without Slowing Innovation

• Conclusion

AI as a Powerful Ally in Cybersecurity Defense

AI isn’t just a tool for attackers — it’s a powerful defensive asset when used right.

Cybersecurity teams are increasingly using AI for threat detection, behavioral analytics, and anomaly detection at a scale humans simply can’t match. Predictive models can sift through millions of events per second, flagging suspicious patterns that traditional tools might miss.

Example: Platforms like CrowdStrike use AI-native models to proactively hunt for threats across trillions of telemetry events, catching threats in real-time before they escalate.

Use Cases of AI in Cyber Defense

Real-Time Threat Detection

AI models can run through tons of telemetry, user behavior, and system noise to flag the kind of anomalies a human would miss until Monday morning, after the breach.

Phishing Detection

Instead of just checking sender names and sketchy URLs, AI scans for off vibes — unnatural language, unusual patterns, and signs that something’s pretending to be something it’s not.

Incident Response

Smart platforms can now auto-quarantine a laptop, revoke credentials, or kill a session early on. It's like having a security analyst who works faster than you can say "containment protocol."

Red-Teaming and Simulation

GenAI is sophisticated enough to closely mimic attackers — drafting fake emails, crafting payloads, even emulating user activity. If you’re not testing your defenses with this, someone else eventually will … and not in the context of a contained experiment. 

AI-powered Cyber Attacks & Other Risks

Here’s the problem: attackers are using the same playbook.

According to CrowdStrike, generative AI is now a go-to tool for adversaries across eCrime, nation-state, and hacktivist categories. It’s used for deepfake business email compromise schemes, spear phishing, disinformation spreading, and a host of other social engineering plays.

Real-World Examples of AI-Powered Attacks

Voice Cloning

According to Crowdstrike, threat actors used deepfaked video clones of a CFO to trick staff into transferring $25.6 million in 2024.

AI-Generated Phishing

A 2024 study showed that LLM-generated phishing emails had a 54% click-through rate — compared to just 12% for human-written ones. 

Obfuscating Existing Malware

Unit42 conducted an experiment where it was able to develop an adversarial ML algorithm that uses LLMs to generate novel variants of malicious JavaScript code at scale. 

Key Risks to Watch:

Prompt Injection and Jailbreaks

Attackers can manipulate model inputs to bypass safety controls or force the AI to behave in unintended ways, potentially exposing sensitive logic or functions. These aren’t theoretical risks — they’re already happening, and most organizations wouldn’t know it until the damage is public or legal.

Data Leakage Through AI Models

Poorly configured models may unintentionally reveal proprietary or personal information embedded in their training data or conversation history. It's like having an intern with a photographic memory and no sense of discretion — and then putting them on the front lines with customers.

Model Poisoning During Training

If training data is compromised, attackers can subtly embed backdoors or biases that degrade the model’s integrity or reliability. In other words, you think you’re building a helpful assistant, but someone else taught it to lie when no one’s watching.

Shadow AI Usage in the Enterprise

Employees may use unauthorized AI tools without IT oversight, creating blind spots in security posture and increasing the risk of data exposure or compliance violations. It's the AI version of bringing a personal device to work and connecting it to everything — except this one could accidentally summarize your entire roadmap and leak it to the internet.

This brings us to a core idea: AI isn’t just a tool or a threat — it’s both. AI is now both the sharpest sword and the weakest shield in cybersecurity.

A Widening Attack Surface — And Shrinking Response Time

One of the biggest impacts of AI? It’s drastically increasing the attack surface while decreasing the time defenders have to respond.

CrowdStrike reports that 79% of threats it detected in 2024 were malware-free, relying on hands-on-keyboard techniques that blend in with legitimate user activity and impede detection. It also reports that breakout times (the time between breach and lateral movement) dropped to an average of 48 minutes, with the fastest clocking in at 51 seconds. 

Meanwhile, organizations are integrating GenAI into everything — from customer chatbots to HR onboarding flows — creating hundreds of new touchpoints for exploitation.

Why This Matters

AI creates new vulnerabilities.

We’re not just talking about buggy code — we’re talking about strange model behaviors triggered by the right (or wrong) prompt, shady plugins that punch holes in your data walls, and training sets that quietly teach the model to go rogue. These issues live in the messy space between tech and human interaction, which makes them almost invisible to your standard security tools.

Most orgs lack visibility into how GenAI is being used internally.

If you don’t know who’s using AI or what they’re doing with it, you’re not managing risk — you’re guessing. Shadow AI isn’t just annoying, it’s dangerous; it creates all the risk of a data leak with none of the audit trail.

Legacy security frameworks aren’t equipped for prompt manipulation or output integrity.

You can’t firewall your way out of a language model exploit. Most traditional controls still assume attacks come in packets or payloads — not clever wording or tricky instructions that get a chatbot to spill company secrets.

Ultimately, organizations are vulnerable to new threats they don't know how to defend against yet. That's why proactive governance is so important.

What Organizations Are Getting Wrong About AI Security

AI’s biggest threat to cybersecurity isn’t technical. It’s organizational blindness.

Even as generative AI becomes more ubiquitous and necessary to business survival, several security leaders are reluctant to embrace it — often leading to impractical (or flat-out stupid) actions like hard bans on LLM adoption. 

According to Forrester, many CISOs "tune out news about new technologies, considering it a distraction … which only drives employees underground, costing the security team visibility and understanding how the tech is used and increasing risks."

Not engaging with AI meaningfully creates a dangerous blind spot. Without proactive oversight, organizations risk losing control over how generative tools are used … and how they might be misused.

Siloed responsibility also often trips businesses up. Cyber teams often don’t loop in legal, HR, or data governance when GenAI is being deployed. But when AI models ingest sensitive data, create bias, or hallucinate misinformation. Those consequences fall far beyond IT.

Common Mistakes

No Cross-Functional AI Risk Governance.

AI isn’t just an IT problem — it touches everything from legal to HR to customer service. When risk decisions live in silos, nobody sees the full picture until something breaks… and by then, it’s usually expensive.

Relying on Basic Third-Party Checklists for GenAI Vendors

Most vendor checklists were written for SaaS tools, not systems that generate novel content from probabilistic models. If you’re asking a GPT vendor the same security questions you’d ask a payroll app, you’ve already missed the plot.

Failure to Monitor AI Inputs and Outputs

You wouldn’t launch a new web app without logging. Why let a language model generate responses — potentially to your customers — without watching what it’s saying or ingesting?

Assuming AI Vendors Handle All the Security

This might be the most comfortable mistake of all. But just because a model lives in the cloud doesn’t mean your exposure ends there, especially when your own data is part of the equation.

Instead, Security Leaders Should:

Embrace a "shift left, expand right, and repeat" mindset.

According to a report from the World Economic Forum, security leaders need to prioritize this one. Security can’t just be a last-minute checkpoint anymore. If you’re not building guardrails into the design phase — and stress-testing post-launch — you’re just hoping nothing blows up.

Build shared ownership across tech, risk, legal, and operations.

If your AI policy only lives in a security deck, it’s already dead. Get the folks who manage risk, process contracts, and ship product in the same room — then make sure they all have skin in the game.

Create clear guidelines on acceptable AI use, monitoring, and third-party tooling.

No one reads a 20-page policy PDF buried on the intranet. You need guardrails people actually understand: what tools are okay, what data’s off-limits, and what happens if someone freelances their own AI stack.

Tips for Securing AI Without Slowing Innovation

Balancing AI innovation with security doesn’t have to mean paralysis. Here’s how smart teams are navigating it:

1. Creating an AI Asset Inventory

Track where and how AI is being used (internally and externally). Yes, even that chatbot your sales team spun up last quarter. If you don’t know what you’ve got, you can’t protect it — or explain it when it breaks.

2. Build a Multidisciplinary Governance Team.

Bring in stakeholders from security, legal, HR, data, and product. AI risk is a team sport now — and your chances of winning drop fast if only IT is on the field.

3. Prioritize Explainability and Monitoring.

You wouldn’t launch a product without logging or metrics. Same rules apply here: if the model spits out something wildly wrong (or wildly offensive), you’d better know why — and who to blame.

4. Red-Team Your Own AI Systems.

Get ahead of this one before the bad guys do it for you. Think like an attacker, test like a pessimist, and assume someone out there is trying to prompt your model into chaos.

5. Treat GenAI Like Any Other Third-Party Vendor.

Just because it’s cool and conversational doesn’t mean it gets a pass. Ask hard questions: Where’s the model hosted? Who can access it? What’s it doing with your data? If your vendor dodges, you’ve got your answer.

Conclusion

Want to hear some revelatory insight you can't get anywhere else? AI is changing lots of stuff — like pretty much everything everywhere (at least in some capacity), and cybersecurity is definitely no exception. 

It's changed the game for both attackers and defenders, and it's only getting more intricate, sophisticated, and potentially disastrous. 

The last thing you want to do is plug your ears, close your eyes, and hope that AI un-invents itself. The organizations that thrive won't be the ones that block AI. They'll be the ones that understand it, govern it, and use it with intention. 

That means building an AI-driven cybersecurity strategy that keeps pace with the threats that might undermine it — one that's as dynamic and intelligent as what adversaries are coming up with … but be careful. 

Let AI help you, but don't trust it blindly. It's come a long way as a resource for developing a sound cybersecurity infrastructure, but there won't be a day when you should lean on it exclusively.