Defending your business from AI-powered threats in 2025

The rapid evolution and scale of AI-powered cyberattacks

Cyberattacks are evolving at lightning speed, and AI is fueling this transformation. What used to be a manual process, painstakingly slow and limited, has become an AI-powered engine capable of launching large-scale attacks faster than ever. It’s happening now, and it’s growing exponentially.

We’re talking about generative AI (gen AI), AI that can create content like text, images, or even video, and attackers are using it as a tool to generate phishing emails, deepfake videos, and social engineering tactics on a massive scale. These aren’t your standard, easily detected attacks. They’re sophisticated, personalized, and hard to spot until it’s too late. Deepfakes, for example, can mimic people convincingly enough to fool seasoned professionals. Deloitte predicts losses from deepfake-related scams will hit $40 billion by 2027, growing at an annual rate of 32%. This isn’t a small issue.

The key targets are financial services, healthcare, and industries managing complex supply chains. Attackers conduct months of reconnaissance, scanning for weak points. before striking. And they don’t need to break your firewall to do it. Often, a simple phone call to your IT helpdesk requesting a password reset is all it takes. Once they’re in, breaches can last for months undetected, giving attackers ample time to compromise sensitive data and systems.

“AI has leveled the playing field for cybercriminals. What’s important now is using AI to outsmart these attacks before they start.”

Using AI to fight AI-powered attacks

You’ve probably heard the phrase “fight fire with fire.” In cybersecurity, it’s more like fighting AI with AI. To put it bluntly, traditional defenses aren’t fast enough to keep up with these rapidly evolving threats. The attackers’ playbook is constantly changing, adapting in real-time. If it takes your team days to analyze data, you’re already too late.

AI-powered defense systems change things by monitoring network activity in real-time, detecting anomalies, and automating responses in minutes, sometimes seconds. These systems prioritize threats, cutting through the noise to focus on the key issues. For instance, they can identify unusual patterns across endpoints and trigger an automatic response like isolating a compromised device or blocking malicious IP addresses without waiting for human intervention. This level of speed and precision is key when attackers can breach and pivot through systems in minutes.

Katherine Mowen, SVP of Information Security at The Rate Companies, summed it up perfectly: “We’re fighting AI with AI because we can’t afford to fall behind.” After watching peers in the mortgage industry suffer serious breaches, her team adopted AI-based defenses to stay one step ahead. It’s the only practical way to stay competitive and protected.

The lack of documented strategy is a major risk

Many organizations don’t have a written plan for defending against AI-driven cyberattacks. If your company falls into this category, you’re not alone, but it’s a risky place to be.

AI-powered threats like phishing, ransomware, and API vulnerabilities are expanding at an unprecedented pace. Without a documented strategy, your security response is likely fragmented, slow, and reactionary. You might have the right tools, but if they don’t work together under a cohesive plan, they become much less effective. A documented strategy creates alignment across your entire organization, making sure that roles, processes, and responses are clear and efficient when an attack hits.

“Ivanti’s 2024 State of Cybersecurity Report found that 60% of security leaders don’t feel prepared for AI-powered threats.”

Real-time data ingestion and AI-driven analysis

Speed is everything when it comes to cybersecurity. Attackers are faster than ever, and if your defenses can’t keep up, you’re playing a losing game. The future of cybersecurity belongs to those who can ingest, normalize, and analyze data in real-time. That’s where AI shines.

Here’s how it works: traditional security teams are overwhelmed by logs and alerts from multiple sources, endpoints, SaaS apps, and on-premise servers. Sorting through all that data manually takes hours, even days. AI cuts that down to minutes by automatically ingesting and mapping data to a common format, which speeds up analysis and makes anomalies easier to detect.

Once the data is normalized, AI-powered triage engines prioritize the most critical incidents, helping security teams focus on high-value leads instead of getting buried under irrelevant alerts. Imagine an AI engine spotting a potential breach, running a full investigation in seconds, and suggesting remediation steps, all without a human lifting a finger. This isn’t a future concept. It’s happening now, and it’s already reducing response times from hours to minutes.

Zero-trust architecture is improved by AI

The best way to think about zero-trust architecture is simple: never trust, always verify. That means every access request, whether it comes from inside or outside your network, is treated as a potential threat until verified. It’s the opposite of the old approach where internal traffic was considered “safe” by default. Zero trust makes sure that attackers can’t move laterally within your network, even if they manage to breach one part of it.

Now, combine that approach with AI, and you’ve got something truly powerful. AI makes zero-trust architecture smarter and faster. It constantly monitors behavior, looking for patterns that suggest something is wrong, like a user accessing data they wouldn’t normally touch or a device acting abnormally. These subtle signs are often the first indicators of a breach. AI catches them and triggers security protocols before attackers can do serious damage.

Another advantage of AI in zero-trust architecture is its ability to predict and block likely attack paths. It analyzes threat intelligence, vulnerabilities, and user permissions to recommend a few targeted fixes that can stop multiple attack routes at once. A proactive approach turns security from a reactive process into a strategic defense.

Without AI, zero trust is still effective, but slower and more prone to human error. With AI, it becomes a dynamic, real-time defense that adapts as the threats evolve. The bottom line: attackers are getting smarter every day. You need a system that can keep up, and that system starts with AI-enhanced zero trust.

12 must-dos to close AI security gaps

If you’re serious about defending against AI-driven cyberattacks, you need a plan that’s as agile as the attackers you’re trying to stop. This is where the 2025 Endpoint Security Playbook comes in, a framework of 12 key strategies that can help organizations bridge the widening gaps in their cybersecurity defenses. Think of it as your roadmap to staying ahead.

Here’s a high-level breakdown of the most critical areas:

Adopt SASE or SSE for unified real-time monitoring: Instead of relying on disconnected security tools, embrace a Secure Access Service Edge (SASE) or Security Service Edge (SSE) framework. These systems bring networking and security together in one cloud-based service, letting AI monitor your entire environment in real time.
Semantic data modeling for unified visibility: Data from endpoints, cloud services, and identity systems often come in different formats, making it hard to analyze quickly. AI solves that by normalizing all data into a single, understandable format, giving your team a full picture fast.
AI-based triage and playbooks for faster incident response: AI-powered Extended Detection and Response (XDR) platforms take incident response to the next level. Pre-built playbooks help your team respond to threats in minutes instead of hours. Imagine isolating an endpoint or blocking malicious traffic with a single click, AI makes that possible.
Signal-like engines for threat prioritization: AI correlation engines can sift through millions of alerts to identify meaningful patterns. This cuts down on noise and helps you focus on real problems, not just false positives. It’s like having a smart filter that shows you the threats that actually matter.
Identity threat prevention with real-time posture checks: Attackers love to exploit stolen credentials, but AI can stop them in their tracks by analyzing login behavior and user permissions in real time. If something looks off, access is blocked instantly.
Proactive hardening through attack path analysis: AI pinpoints weak spots in your infrastructure and recommends targeted fixes that block multiple attack paths at once. A proactive hardening approach reduces the chances of an attacker moving laterally.
Explainable AI and governance: Trust in AI is key, especially at the board level. Make sure your AI systems offer full transparency, no black boxes. This means every AI-driven decision can be traced and explained.
Specialized AI over generic models: Use AI models trained on real-world attack tactics to improve detection accuracy and reduce false positives. Generic models are too broad for the complexity of endpoint security.
Continuous model updates and dataset refreshes: Threats evolve constantly, so your AI needs to stay updated. Regular model tuning makes sure your security stays ahead of the latest attack trends.
Human-in-the-loop validation: AI is powerful, but human oversight is still invaluable. Analysts play a key role in refining AI findings and catching nuanced threats that automation might miss.
Automated incident response orchestration: Integrate AI-driven incident response playbooks with your zero-trust architecture. Once validated, responses should propagate across endpoints, firewalls, and identity systems instantly.
End-to-end zero-trust integration: Build security into every step of the kill chain. AI detection, combined with strict access controls, makes attackers fight for every inch of progress, if they can progress at all.

“The ultimate goal here is to reduce dwell time (the time attackers remain in your system) and speed up containment, transforming your security operations from reactive to proactive.”

Final thoughts

AI-powered attacks are growing in sophistication and frequency, but so are the tools we have to fight back. The difference between those who thrive and those who struggle will come down to one thing: speed of adaptation.

A well-executed AI-driven strategy, built on zero-trust principles and real-time analysis, can turn the tide. Don’t wait until the threat becomes unmanageable. Build your defense today, because in cybersecurity, there’s no such thing as being too early, but being too late can be catastrophic.

Key executive takeaways

Rapid attack evolution: AI-powered cyberattacks are scaling in speed and sophistication, exploiting endpoints with precision. Decision-makers must acknowledge this trend and invest in defenses that evolve as rapidly as the threats.
Real-time AI defense: Traditional security measures fall short against fast-moving AI-enabled attacks. Leaders should prioritize AI-based systems that ingest, normalize, and analyze data in real time to minimize breach detection and containment times.
Zero-trust integration: A zero-trust approach, where every access request is continuously verified, curtails lateral movement once a breach occurs. Executives should enforce this model alongside AI monitoring to strengthen overall network security.
Comprehensive security playbook: A holistic strategy is invaluable, combining real-time data processing, automated incident response, and human oversight. Decision-makers must implement an integrated playbook for a resilient, proactive defense against evolving AI threats.