From trojans, viruses, and worms to malware, phishing, and insider threats. Cybersecurity measures are growing more powerful thanks to AI. But so are cyber attacks. Which side is winning? Let’s figure it out.
In the 1980s, a well-known biologist and PhD named Dr. Popp distributed floppy disks carrying a Trojan horse virus. The first commercial antivirus software for computers hit the market in 1987. Obviously, technology has evolved enormously since Dr. Popp’s era. Nowadays, corporate cybersecurity includes regulations like GDPR, employee access privileges, and dedicated security monitoring tools. Artificial intelligence enhances these standard protective measures. It has given organizations far better defense against online criminals. At the same time, cybercriminals have converted AI into an offensive tool.
In this outline, we discuss the latest tendencies in cybersecurity together with our AI development experts. What is AI in cybersecurity? How to use AI in cybersecurity? How are criminals using it? What are the successful cases of AI applications against cyber attacks that IT professionals can learn from? What is going to happen next? These are some of the issues that are discussed further.
Why AI is critical for modern defense
According to the National Cyber Security Center Report that covers the period from 2025 to 2027, AI is making cyber attacks more efficient and effective. Critical systems (e.g., national infrastructure) tend to lag behind the cybersecurity mitigations, which puts them at risk of becoming more vulnerable to advanced attackers by 2027. Integration of AI models into critical infrastructures in combination with insufficient training in safe use might provide attackers with new entry points (e.g., prompt injection, software vulnerabilities, etc.).
The above-mentioned report emphasizes that AI is unlikely to add new unknown types of attacks, but rather enhance existing methods through the increased volume and impact. Attackers are almost certainly already using AI for:
- Victim reconnaissance: analyzing the publicly available data from a specific company or industry, such as key employees and their roles, technologies used, internal document exposure, etc. It creates a detailed “victim blueprint” automatically.
- Vulnerability research & exploit development: scanning the code for vulnerabilities and generating a path to exploit it.
- Social engineering / phishing: sending personalized and convincing emails to thousands of employees simultaneously. Criminals use AI to mimic a CEO’s writing style or clone their voice, tricking individual employees, or even entire teams, into disclosing sensitive data such as passwords, financial information, or internal documents.
- Basic malware generation: creating functional malware without understanding code. AI has made hacking obtainable to anyone with malicious intent. It also allows rapid adaptation: when one version is detected, the attacker generates a new, different version in seconds.
- Processing stolen data: searching, classifying, and extracting value from unstructured data. Attackers use AI to quickly find valuable information after a breach.
AI in action: success stories
A successful AI integration begins with a comprehensive discovery phase. During this stage, AI consulting experts analyze a company’s specific business objectives and risk profile to design a tailored and intelligent cybersecurity strategy. Below are real-world examples of how specialized AI applications in cybersecurity are protecting modern businesses:
Security awareness training: HSBC & Wunderman Thompson
How do most employees treat cybersecurity training? “It’s just another task that I need to put a tick on and forget about it right the next minute”. To combat that type of attitude, HSBC decided to go forward and impress viewers with realistic faces of the criminals to trigger a feeling of genuine threat.
HSBC partnered with Wunderman Thompson and Carnegie Mellon University to utilize a “voice-to-face” AI neural network. They fed the AI actual voice recordings of scam calls. The algorithm matched the voice characteristics with physical traits (jaw structure, nose shape, age, ethnicity, etc.) to predict what the fraudsters looked like with 80% accuracy. Then, digital faces of criminals were used to record tutorial videos that warn against scam calls.
The result: The campaign achieved a 66.5% View Through Rate on YouTube and a 32.2% VTR on TikTok, which was twice more than the expected benchmark.
Identity & access: NatWest Bank & BioCatch
Traditional authentication (passwords, PINs, or standard SMS MFA) only proves that the credentials are correct at the moment of login. If a fraudster steals a session or uses a Remote Access Trojan to hijack an authenticated device, traditional security assumes the user is legitimate and lets them access.
NatWest, one of the UK’s largest banks, partnered with BioCatch to deploy AI-driven behavioral biometrics. Instead of looking at what the user knows (passwords), the AI looks at how the user acts. The algorithm analyzes over 500 physical and cognitive data points during a banking session. This includes typing cadence, swipe patterns, hand-eye coordination, the angle the phone is held, how hard the screen is pressed, and even natural hand tremors. This information forms a unique “muscle memory” profile for the customers. If an attacker successfully logs in using stolen credentials, their physical interactions with the app will not match the victim’s biometric profile.
The result: NatWest reported that the system successfully identifies automated bots, RATs, and human impostors mid-session, allowing the bank to halt fraudulent fund transfers before the money ever leaves the account.
Threat & anomaly detection: McLaren Racing & Darktrace
Signature-based security tools only stop known threats. As malware rapidly evolves and attackers use sophisticated supply-chain tactics to compromise internal systems, security teams are often left playing catch-up.
McLaren Racing utilizes Darktrace, an AI cybersecurity platform that acts like a digital immune system. It detects subtle anomalies in the emails or network behavior by comparing them to historical interactions with specific suppliers or other partners.
The result: When McLaren is targeted by attackers who send legitimate-looking but malicious voicemails or links to the staff, Darktrace’s software detects it and blocks without human intervention.
Phishing filtering: Abnormal Security
Business Email Compromise and advanced phishing campaigns are bypassing traditional Secure Email Gateways. Attackers are increasingly using the “Living-Off-Trusted-Sites” (LOTS) strategy. They host malicious payloads on legitimate platforms like Google Drive, Canva, or cloud-based AI tools, so security filters don’t flag the URL as malicious.
Abnormal Security uses a cloud-native, API-based architecture equipped with NLP and behavioral AI to analyze the context, relationships, and sentiment of emails, rather than just scanning links.
The result: Abnormal’s behavioral AI catches attacks by establishing baselines for how employees usually communicate. The AI flags the threat by analyzing a combination of signals: the subtle shift in tone, an unusual request (the CTA button), and the login anomaly of the original sender. By correlating thousands of data points across the cloud environment, the AI intercepts the socially engineered attack before it reaches the inbox.
Cost of implementation
Companies interested in Generative AI integration services, ML for fraud detection services, and other related domains want to make sure every dollar spent on the digital transformation is worth it. In other words, businesses need to see if the investment really results in cost savings or drives revenue growth. According to the IBM report, companies that spent money on AI-powered safety solutions managed to save $1.9M in 2025. The same report also reveals that 63% of breached organizations lack AI governance policies, and shadow AI (unauthorized employee use) added an extra $670,000 to breach costs. These statistics reinforce the idea that companies need to invest properly in governed AI security solutions rather than leaving AI usage unmanaged.
To understand this investment, it is helpful to break down the primary expenses involved. Here’s a table summarizing the cost structure of the AI in cybersecurity implementation.
| Cost component | Description | Estimated range (annual) |
| Software licensing | SaaS platforms (EDR, SIEM, NDR) with embedded AI/ML modules. | $20,000 – $500,000+ |
| Infrastructure | On-prem GPU clusters for low-latency inference; cloud compute costs for training models. | $50,000 – $1M+ |
| Data engineering | Cleaning, labeling, and preparing internal data for custom model training. | $30,000 – $150,000 |
| Talent & training | Hiring Data Scientists, AI Security Engineers; upskilling SOC analysts. | $150,000 – $400,000 (per senior engineer) |
You might hesitate given such substantial initial financial and operational investment. However, the ROI outweighs the costs of relying on legacy systems. Traditional security measures typically depend on static, signature-based detection rules, which are entirely reactive. It means they can only catch threats they have seen before. This leaves networks highly vulnerable to new zero-day attacks and overwhelms human experts with alert fatigue from false positives. AI, on the other hand, acts as a force multiplier. It analyzes massive datasets and detects anomalies in real-time, blocks threats in seconds, and minimizes false positive alerts. As a result, AI automation in cybersecurity actively neutralizes attacks and demonstrates a higher efficiency in comparison with traditional security systems.
| Metric | Traditional security | AI-powered security |
| Mean Time to Detect (MTTD) | Days to weeks | Seconds to minutes |
| Mean Time to Respond (MTTR) | Hours | Automated (milliseconds) |
| False Positive Rate | High (30-50%) | Low (optimized models reduce by 70-90%) |
| Security Team Efficiency | Overwhelmed by alerts | Strategic focus on complex threats |
AI as a weapon for attackers
The mind-boggling financial losses seen in the Arup and Retool breaches highlight a chilling reality: AI has become a highly effective weapon in the modern cybercriminal’s arsenal. AI technologies help attackers to bypass traditional human verification and security protocols. They also enhance the scale and precision with AI. For every defensive measure, threat actors are developing an AI-driven countermeasure. Here’s how it happens on a few examples:
| Function | Defenders | Attackers |
| Phishing | AI detects anomalies in sender behavior and context. | GenAI writes perfect, culturally relevant phishing lures. |
| Malware | AI analyzes file behavior in sandboxes. | AI generates polymorphic code to evade detection. |
| Credentials | AI monitors for unusual login locations/behavior. | AI automates credential stuffing with bypass CAPTCHA models. |
| Vulnerabilities | AI prioritizes patching based on exploit probability. | AI scans code repositories for zero-day exploits. |
The future of AI in cybersecurity
According to Gartner, AI is evolving rapidly, yet many tools are being deployed before they are fully tested. This rush to innovate is exactly what will shape the next decade of digital defense.
Let’s look closer at the forecasts. By 2028, 50% of incident response efforts will involve custom-built AI applications. That means security teams will face incidents caused by tools that were rushed into production without clear security protocols. To address this situation, over half of all enterprises will be forced to adopt dedicated AI security platforms. These platforms will be powerful enough to cope with risk management and defend against complex, emerging threats like prompt injection and data misuse.
Another staggering metric is that by 2030, 33% of IT work will be spent remediating “AI data debt.” Therefore, unstructured and poorly secured data remains a massive barrier to safe AI adoption. The regulatory pressure (fines exceeding 5% of the global revenue due to manual AI compliance processes) contributes to the necessity of automated governance and data readiness.
To combat these challenges, 70% of CISOs will turn to AI-powered identity visibility by 2028. Agentic AI in cybersecurity will further transform threat hunting, patch management, and incident response. Other technological changes include fully autonomous security operations centers, where threats will be eliminated automatically, and Explainable AI (XAI) that will guarantee that AI-powered security decisions are transparent and legally auditable.
Discover the AI cybersecurity trends for the next five years:
| Trend | Impact level | Timeline |
| Autonomous response (SOAR) | High – replaces manual incident response | 1-2 Years |
| AI-driven data privacy compliance | Medium – automated data mapping and GDPR/CCPA enforcement | 1-3 Years |
| Deepfake detection as standard | Critical – integrated into all video conferencing and biometric tools | 2-3 Years |
| Generative AI for red teaming | High – AI automatically tests defenses by simulating attacker behavior | Currently emerging |
Conclusion
Today, artificial intelligence is revolutionizing cybersecurity. AI has become the most powerful tool available to defenders, but on the backside, its accessibility also lowers the barrier to entry for cybercriminals. Businesses understand the necessity of saving their assets and reputation. AI in cybersecurity is augmenting human expertise and making it easier for cyber experts to react to risks and swiftly enforce the defence mechanisms.
At PixelPlex, we implement tailored AI cybersecurity solutions that fit our clients’ specific infrastructure and budget. If you are hesitating regarding how to use AI in cybersecurity, contact our team. We will be happy to make your business protected and safe for your clients.
