AI and Cybersecurity: Threats, Defenses, and What to Expect Next

Artificial intelligence (AI) is reshaping cybersecurity. It’s being used to defend networks and to launch more sophisticated attacks. AI and cybersecurity are now closely connected, for better and worse.

As threats become more advanced and harder to detect, it’s crucial for organizations to understand how AI is changing the way we protect systems—and how to use it wisely. This isn’t just about adopting smarter tools; it’s about rethinking how we approach risk, respond to incidents, and stay resilient in a fast-moving, automated world.

AI and cybersecurity

The rapid evolution of AI has brought powerful new technologies into today’s security workflows. Tools powered by deep learning, large language models (LLMs), reinforcement learning, and generative AI help teams detect patterns in real time, anticipate threats, and even take autonomous action when needed.

Over the past decade, AI has advanced dramatically. Deep learning models now outperform traditional algorithms in tasks like image and speech recognition. Natural Language Processing (NLP) has taken major leaps, thanks to transformer models like BERT and GPT, making it easier for machines to understand and generate human language.

Techniques like reinforcement learning allow AI systems to adapt and learn from experience. Meanwhile, edge AI is bringing real-time decision-making to devices like smartphones and IoT sensors. Together, these innovations are laying the groundwork for more responsive, intelligent, and autonomous security systems.

AI-powered cyber threats

As AI becomes more accessible, attackers are weaponizing it to create more targeted, evasive, and scalable cyberattacks. Below are four major threat types that highlight the risk:

AI-powered malware: DeepLocker

One of the earliest examples of AI-driven cyber threats is DeepLocker, a proof-of-concept malware developed by IBM. Unlike traditional malware, DeepLocker uses AI to hide, identify, and strike, all while remaining undetected until the perfect moment.

Here’s how it works:

• Concealed payload: The malicious code is hidden inside a benign-looking application (like video conferencing software). It’s encrypted, making it nearly impossible to detect with traditional antivirus or scanning tools.
  
• Target identification: DeepLocker uses AI models trained to recognize specific attributes such as:
  • Facial recognition data
  • Geolocation
  • Voice recognition
  • System configuration
    
• Trigger conditions: The malware stays dormant until it confirms it has reached its intended target. Only when all conditions are met will it decrypt and activate the payload.
  
• Attack execution: Once triggered, the malware executes its payload, which could involve:
  • Deploying ransomware
  • Exfiltrating sensitive data
  • Taking control of the system

Why DeepLocker is so dangerous

• Evades detection: Because it’s embedded in a legitimate application and encrypted, DeepLocker is extremely difficult to identify with conventional security tools.
  
• Highly targeted: It only activates for a specific user or environment, reducing its exposure and increasing the success rate of the attack.
  
• Hard to attribute: Its stealthy behavior makes it difficult to trace back to its origin, making attribution challenging for security teams.

What this means for defenders

DeepLocker illustrates a new class of threats: AI-powered malware that adapts, evades, and strikes with precision. It’s a wake-up call for cybersecurity teams. Defending against these threats requires advanced, AI-driven detection methods that go beyond signature matching and static rules. Defenders need to protect their apps from tampering and reverse engineering—especially with embedded AI logic. Learn more about application hardening.

Data poisoning attacks

Data poisoning attacks involve injecting malicious or misleading data into a machine learning model’s training set. This manipulation corrupts the model’s output and can lead to incorrect or dangerous behavior, especially in critical systems like autonomous vehicles, fraud detection, or recommendation engines.

How it works

• Malicious data is added to the training dataset.
• The model learns flawed patterns from this data.
• Once deployed, it produces unreliable or exploitable results.

Common tactics

• Targeted manipulation: Ensuring specific harmful inputs go undetected (e.g., phishing emails).
• Noise injection: Reducing overall accuracy to undermine trust in the model.

Examples

• Self-driving cars misreading traffic signs due to altered physical inputs.
• Recommender systems being skewed by fake user behavior or biased data.

Why it matters

• Undermines model reliability and safety.
• Damages trust in AI systems.
• Requires strong defenses like data validation, anomaly detection, and robust training algorithms.

Evasion attacks

Evasion attacks refer to techniques used by adversaries to bypass detection by AI-based systems, particularly classifiers. These attacks exploit vulnerabilities in machine learning models by subtly altering malicious inputs, causing the system to misclassify them and allowing threats to slip past security defenses unnoticed.

How it works

• Model probing: Attackers study how the model works, what features it looks for, and how it draws boundaries between safe and unsafe inputs.
• Crafting evasive inputs: They modify data (e.g., malware, images, phishing emails) just enough to avoid detection while keeping the original attack intact.
• Exploiting weaknesses: These manipulated inputs bypass defenses or cause the model to make incorrect decisions.

Common techniques

• Adversarial examples: Adding slight, imperceptible noise to inputs to mislead classifiers (e.g., tricking image recognition models).
• Feature reduction: Removing traits that typically trigger alarms, like malware signatures or behavioral flags.
• Benign mimicry: Designing threats (like phishing emails) to closely resemble safe content.

Real-world examples

• Image misclassification: Researchers once fooled a neural net into labeling an image of a cat as guacamole by changing just a few pixels.
• Malware evasion: Attackers often encrypt or modify malware to make it appear harmless to AI-powered security tools.

Why it matters

• Let attackers bypass AI defenses without raising suspicion.
• Undermines trust in AI-powered security tools.
• Requires proactive mitigation, including:
  • Adversarial training
  • Continuous monitoring and model updates
  • Defense-in-depth strategies

Automated exploit generation (AEG)

AI can now discover and exploit software vulnerabilities automatically and at a scale and speed that human hackers simply can’t match. 

How AEG works

• Code analysis: The system analyzes software using:
  • Static analysis (examining code without running it)
  • Dynamic analysis (observing runtime behavior)
• Vulnerability detection: It searches for flaws like:
  • Buffer overflows
  • Use-after-free errors
  • Injection vulnerabilities
• Exploit creation: Once a flaw is found, the system crafts inputs to trigger the issue, often enabling unauthorized code execution or system compromise.
• Verification: The exploit is tested to ensure it works reliably against the target.

Use cases and implications

• Cybersecurity research: Helps researchers identify and patch vulnerabilities faster and at a greater scale.
• Offensive security: In the wrong hands, AEG can be used to exploit systems maliciously, raising the stakes for responsible use and ethical disclosure.
• Secure development: DevOps teams can integrate AEG into their pipelines or follow secure software development best practices to detect bugs before release.

Benefits of AI for cybersecurity defense

AI significantly impacts cybersecurity by automating and improving many aspects of threat detection, analysis, and response. Below are some of the key ways AI is being used to enhance security across modern systems and workflows:

• Automated vulnerability detection: Machine learning models can scan codebases to uncover vulnerabilities and even predict where new ones may emerge.
• Smarter code analysis: AI reduces false positives and negatives in static analysis by learning from context and past data.
• Security testing automation: AI-driven fuzzing and penetration tools can generate smarter test cases to expose hidden vulnerabilities.
• Anomaly detection: By learning normal behavior, AI can flag unusual patterns in logs and user activity that may indicate a breach.
• Threat intelligence: AI sifts through vast data sources to surface patterns and correlations, enabling early threat detection.
• Automated response: AI can isolate compromised systems, block malicious IPs, or suggest code fixes in response to incidents.
• Stronger authentication: Behavioral biometrics like keystroke patterns and mouse movement enhance identity verification.
• Custom security policies: AI tailors policies based on past behavior, balancing risk without over-restricting users.
• Security chatbots: AI-powered assistants let teams interact with security tools through natural language queries and commands.
• Training and awareness: Adaptive AI-driven training helps teams build skills where they’re most needed.

New Security Risks Introduced by AI Systems

While AI offers powerful advantages for cybersecurity, it also creates new risks that organizations need to address.

AI models themselves can be targets. If attackers manipulate their training data, input, or behavior, these models may produce flawed results or make dangerous decisions. That’s why it’s critical to protect not just your infrastructure, but the AI itself. Learn more in this post on AI in application security.

Key risks include:

• Biased or flawed training data: If an AI system is trained on incomplete or unbalanced data, it may develop blind spots or make unfair decisions.
  
• Adversarial inputs: Attackers can craft inputs specifically designed to confuse AI systems, tricking them into making incorrect predictions or classifications.
  
• Lack of transparency: Many AI models (especially deep learning ones) are difficult to interpret, making it hard to explain decisions or verify accuracy.
  
• Data poisoning: If attackers corrupt the data used to train AI models, they can subtly alter the model’s behavior or reduce its effectiveness.
  

To address these challenges, organizations should adopt best practices for responsible AI development, such as validating training data, testing models for bias, implementing monitoring and alerts, and ensuring human oversight.

The role of AI in the cybersecurity workforce

AI isn’t replacing security professionals; it’s empowering them. AI tools can:

• Triage alerts, prioritize threats, and reduce manual effort
• Simulate attacks for red/blue team exercises
• Enable natural language interaction with security systems (e.g., chatbots for querying alerts or issuing commands)

As cybersecurity threats continue to evolve, the human element remains essential. AI can analyze patterns at scale, but human analysts still interpret signals, assess business context, and make final judgment calls. This partnership between people and intelligent systems will define the next generation of cybersecurity resilience.

AI also supports continuous learning through adaptive training modules, helping security teams stay up to date with the latest threats and technologies. These tools are especially valuable for understaffed or overburdened security teams trying to maintain readiness in a constantly shifting threat landscape.

Bottom line

AI is rapidly reshaping the cybersecurity landscape. While it empowers attackers to create more evasive threats, it also gives defenders the tools to detect, respond to, and prevent attacks with greater speed and precision.

To keep pace, organizations must adopt a layered, AI-augmented security strategy grounded in governance, automation, and human oversight.

Want the full picture? Download the full ebook to explore how AI is influencing cybersecurity and application security, from threat detection to defense strategies.

Looking to strengthen your AppSec program? Kiuwan is a comprehensive application security platform that empowers developers to find and fix vulnerabilities early, enforce coding standards, and secure open-source components. Trusted by global brands for over 20 years, Kiuwan supports 30+ languages and integrates with leading DevOps pipelines so your team can deliver secure software with confidence. Request a free demo of Kiuwan today! 

FAQs

1. How is AI used in cybersecurity today?

The use of AI in cybersecurity has expanded rapidly, helping organizations detect security threats, reduce response times, and improve incident response. AI algorithms can monitor network traffic, flag anomalies, and even recommend remediation steps—making them a key part of modern security operations.

2. What are the biggest risks of AI in cybersecurity?

AI introduces new challenges. Cybercriminals now use deepfakes, adversarial inputs, and data poisoning to bypass traditional defenses. At the same time, poorly trained AI technologies can produce biased results, miss emerging threats, or expose sensitive information if not managed with proper risk management practices.

3. How does AI improve the work of cybersecurity professionals?

AI helps cybersecurity professionals by automating repetitive tasks and surfacing high-priority alerts. With AI-enabled tools, security analysts can focus on strategic decision-making instead of sifting through noise. These tools also optimize incident response by rapidly identifying affected systems and potential attack vectors.

4. What’s the difference between traditional and AI-enabled security tools?

Traditional tools like firewalls rely on signatures and predefined rules. In contrast, AI-enabled tools adapt by learning from vast amounts of data and can detect anomalies in real time. They help organizations maintain a stronger security posture across the entire application lifecycle.

5. Can AI help prevent human error in cybersecurity?

Yes. Since human error is a major cause of breaches, AI can help by automating code reviews, spotting misconfigurations, and reinforcing data privacy policies. This reduces the likelihood of accidental exposures or missteps that leave systems vulnerable.

6. How does Kiuwan support AI-driven cybersecurity?

Kiuwan is a comprehensive security solution that helps organizations build secure software from the ground up. While it doesn’t use AI directly, it complements AI in cybersecurity by providing SAST to detect vulnerabilities early in the lifecycle. This supports a proactive approach to cloud security, secure AI deployment, and overall risk management.