Why Traditional Antivirus Fails Against Zero-Day Threats

Executive Summary

Traditional antivirus solutions, long considered the cornerstone of endpoint protection, are

increasingly ineffective against today’s most dangerous cyber threats: zero-day attacks. These attacks exploit unknown vulnerabilities, often bypassing signature-based detection entirely.

This whitepaper explores why legacy antivirus technologies are no longer sufficient, the

rising threat of zero-day exploits, and how modern approaches such as predictive AI-based

endpoint protection are reshaping cyber defence.

Table of Contents

1. Introduction

2. The Rise of Zero-Day Threats

3. Why Traditional Antivirus Falls Short

4. Case Studies: The Real-World Impact

5. The Shift Toward Predictive, AI-Driven Security

6. Klaatu IT Security: A Modern Approach

7. Recommendations

8. Conclusion

1. Introduction

The threat landscape has evolved dramatically over the last decade. Attackers no longer rely

on known malware strains or repeatable tactics. Instead, zero-day threats—exploits that target previously unknown vulnerabilities—are on the rise. These attacks render traditional

antivirus (AV) solutions obsolete, as their reactive nature leaves a dangerous gap between

threat emergence and detection.

In this whitepaper, we explain why traditional AV is no longer capable of protecting

organisations against advanced threats and offer guidance on adopting next-generation

defences.

2. The Rise of Zero-Day Threats

Zero-day vulnerabilities are security flaws that are unknown to the software vendor and for

which no official patch exists. Cybercriminals exploit these gaps before developers can

respond, making zero-day attacks incredibly potent.

Key Statistics:

• 2024: More than 1,000 zero-day vulnerabilities were reported globally.

• 80% of successful breaches in 2025 involved either a zero-day or social engineering

• component.

• Average time to patch a new vulnerability: 45-60 days

Nation-states, cybercrime syndicates, and opportunistic hackers use zero-days to infiltrate

even well-defended networks, often with devastating financial and reputational

consequences.

3. Why Traditional Antivirus Falls Short

Traditional antivirus solutions operate on three core mechanisms:

1. Signature-based detection

2. Heuristics (rule-based patterns)

3. Manual updates and user reports

While effective against known malware, these methods suffer major limitations:

a) Signature Dependence

Traditional AV relies on recognising known malware signatures. Zero-day threats, by

definition, have no known signature.

b) Slow Response Time

It can take hours or days for traditional vendors to create and distribute updates once a new

threat is discovered.

c) High False Negative Rate

Because zero-days behave differently and unpredictably, AVs often miss them entirely.

d) Incompatibility with Modern Attack Vectors

Modern threats often involve memory-resident malware, fileless attacks, or polymorphic

code—none of which traditional AV can reliably detect.

4. Case Studies: The Real-World Impact

Case Study 1: MOVEit Vulnerability (2023)

A zero-day in the popular file transfer tool MOVEit was exploited to breach thousands of

organisations, including major UK retailers and councils. Traditional AV tools failed to

detect the malicious activity because the exploit was novel and targeted the application layer.

Case Study 2: M&S Ransomware Attack (2024)

A reported £300 million in losses was attributed to a zero-day exploit in the retailer’s

outdated endpoint infrastructure. Signature-based tools were unable to flag the malicious

payload until after it executed.

Case Study 3: Healthcare Breach in Scotland (2025)

A zero-day exploit in an IoT medical device allowed attackers to infiltrate hospital systems

undetected for weeks. No AV alerts were triggered

5. The Shift Toward Predictive, AI-Driven Security

To counter zero-day threats, organisations must move beyond reactive defences. Predictive,

AI-based security platforms offer several advantages:

a) Behaviour-Based Detection

Machine learning models can spot anomalies in user or system behaviour, flagging unknown

threats before execution.

b) Pre-Execution Prevention

Deep learning engines can evaluate binary code and stop malicious files before they run,

without requiring a signature.

c) Continuous Learning

Modern AI systems improve with time, adapting to new techniques and attack surfaces.

d) Low False Positives

Advanced algorithms reduce alert fatigue, improving response speed and confidence.Platforms like Deep Instinct, SentinelOne, and CrowdStrike exemplify this shift to AIpowered protection.

6. Klaatu IT Security: A Modern Approach

Klaatu IT Security offers endpoint protection that outpaces traditional AV. Our solutions

include:

• Next-gen EDR and XDR with predictive threat prevention

• 24/7 MDR services to detect and respond to anomalous behaviour in real time

• Threat intelligence that leverages AI for pre-execution decisioning

• Risk-based user training and phishing simulations to tackle human factors

With solutions that start at less than £3 per user/month, even small businesses can afford bestin-class defence.

7. Recommendations

1. Audit existing endpoint tools for coverage gaps.

2. Retire signature-based-only AV platforms and replace them with AI-driven

3. alternatives.

4. Implement pre-execution prevention capabilities.

5. Train employees to identify and report suspicious behaviour.

6. Partner with a specialist MSSP like Klaatu for end-to-end protection.

8. Conclusion

Zero-day threats are not going away—they are accelerating. Traditional antivirus solutions

cannot keep up, leaving organisations dangerously exposed. A proactive, AI-based approach to endpoint protection is now essential.

Klaatu IT Security empowers UK businesses to defend against the unknown, with predictive

tools that neutralise threats before they take hold.

Don’t wait for the breach. Prevent it.