Understanding Offensive AI vs. Defensive AI in Cybersecurity

Since the public release of ChatGPT two years ago, the field of artificial intelligence has grown tremendously in both good ways and bad. There’s no denying that the rapid evolution of AI has brought transformative benefits to numerous fields, including cybersecurity. But it has also introduced new challenges as malicious actors harness AI for offensive purposes—using it to attack people and organizations with greater sophistication and volume. This offensive AI used by cybercriminals and defensive AI—used by organizations to protect themselves—now represent two sides of the same coin in a growing AI arms race.

Cybercrime is now a trillion-dollar industry, with damages expected to hit $10.5 trillion by 2025. To combat these threats, innovative organizations are now turning to advanced AI systems to stay ahead of attackers. Let’s dive into the role of AI in cybersecurity, how malicious actors are leveraging offensive AI, and the innovative ways defensive AI is countering these tactics.

AI in cybersecurity refers to the use of machine learning and artificial intelligence to detect, manage, and respond to cyber threats. By analyzing vast amounts of data, AI enables faster threat detection, improves accuracy, and streamlines incident response. For example, AI systems can balance security and user experience by analyzing login risks, detecting phishing attempts, and reducing fraud costs. AI also ensures robust defenses against social engineering attacks, malware, ransomware, and other malicious activities—reinforcing the security posture of organizations.

AI’s ability to automate processes makes it invaluable, and particularly when it comes to cybersecurity. It helps identify anomalies in real time, classify and prioritize events, and even generate actionable recommendations for security teams. But the same things that make it useful for cybersecurity professionals unfortunately also make it useful for cybercriminals themselves.

Learn More:

• Cutting Through the Hype: How AI Truly Enhances Cybersecurity

• The Rise, Use, and Future of Malicious Al: A Hacker's Insight

• Webinar Series: The Convergence of AI + Cybersecurity

Offensive AI refers to the malicious use of AI technologies to carry out cyber attacks. Cybercriminals leverage AI’s ability to scale, adapt, and personalize attacks—making them more efficient and harder to detect.

Within weeks of the release of ChatGPT, hackers were using the generative AI tool to create realistic phishing emails to trick victims into sharing sensitive information. And over the last two years, these AI-powered attacks have significantly increased the effectiveness of phishing campaigns, as they often enable criminals to bypass traditional detection systems.

Types of Offensive AI Tactics Used

• Phishing Emails: AI enables the creation of hyper-realistic and personalized phishing emails that mimic legitimate communications. These messages often include dynamic content that adjusts based on the recipient's behavior or data.

• Voice Cloning (Vishing): AI tools can clone voices with uncanny accuracy, allowing attackers to impersonate trusted individuals over the phone. These calls often exploit emotions like urgency or fear to manipulate victims.

• Deepfakes: Using generative adversarial networks (GANs), attackers can produce synthetic media like videos or images that appear authentic. These deepfakes have been used in corporate scams and disinformation campaigns, including one that cost a Hong Kong company more than $25 million earlier this year.

• Adaptive Malware: Reinforcement learning enables malware to "learn" from failed attacks, adjusting its methods to evade detection. This adaptability makes traditional malware defenses obsolete and enables attackers to continuously infiltrate organizations with new versions of the malicious code.

Why Offensive AI is Successful

Offensive AI is effective because it automates complex attack strategies, allowing attackers to execute campaigns at an unprecedented scale. It can:

• Adapt: Machine learning models enable attackers to quickly and easily change tactics, payloads, and delivery mechanisms to bypass security systems.

• Personalize: Offensive AI analyzes a victim’s digital footprint to craft highly convincing attacks, such as tailored phishing emails or unique deepfake communications.

• Scale: Automation powered by AI lets attackers launch thousands of personalized attacks simultaneously, overwhelming traditional security measures and the humans who support them.

How Offensive AI Might Evolve

Looking ahead, malicious actors are expected to advance their use of AI by:

• Automating Social Engineering Attacks: Offensive AI will further refine social engineering tactics by analyzing social media, emails, and other digital footprints to craft even more convincing impersonations and attacks.

• Leveraging AI for Real-Time Adaptation: Offensive AI could continuously monitor defensive systems in real-time, allowing attackers to alter their strategies mid-attack to bypass newly implemented defenses.

• Scaling Ransomware Campaigns: AI may be used to automate and scale ransomware attacks, identifying vulnerabilities in systems faster and customizing ransom demands based on the target's perceived ability to pay.

• Attacking Supply Chains: Offensive AI could infiltrate software supply chains, injecting malicious code into widely used software updates to propagate attacks on a massive scale.

In contrast to offensive AI, defensive AI is the use of machine learning and AI technologies to proactively detect, prevent, and respond to cyber threats—whether powered by AI or created manually by humans. It provides organizations with the ability to anticipate attacks, analyze risks, and automate threat responses in real time. Unlike traditional methods, AI can detect subtle anomalies that indicate emerging threats at scale, making it critical to preventing this next generation of targeted threats.

Types of Defensive AI Tactics Used

• Anomaly Detection: Machine learning models establish baselines of normal behavior to detect deviations. This is crucial for identifying novel threats that lack pre-existing signatures.

• Behavioral Analytics: User and entity behavior analytics (UEBA) profiles typical user actions to flag unusual activities, such as compromised accounts or insider threats.

• Automated Threat Response: AI systems can isolate infected systems, block malicious traffic, and generate detailed incident reports without human intervention.

• Continuous Learning: Defensive AI constantly evolves by analyzing new attack patterns and reinforcing its defenses, ensuring it stays ahead of attackers even as they continuously evolve their tactics.

How Defensive AI Combats Offensive AI

Defensive AI is particularly effective against offensive tactics due to its ability to:

• Detect Anomalies: Unlike traditional signature-based methods, anomaly detection recognizes even subtle irregularities, making it effective against sophisticated threats like socially-engineered emails, deepfakes, and adaptive malware.

• Predict Attacks: By analyzing historical data, defensive AI identifies vulnerabilities and potential attack vectors, enabling organizations to block attacks on end users and prevent breaches proactively.

• Scale Protection: Defensive AI can monitor extensive networks and distributed environments without the need for additional human resources.

Defensive AI also empowers organizations to combat specific threats, such as using non-digital identity verification methods to detect deepfakes or deploying unique code words to prevent voice-cloning scams.

How Defensive AI Might Evolve Cybersecurity Efforts

As cyber threats become more advanced, defensive AI will continue to adapt by:

• Focusing on Proactive Defense: Multiple cybersecurity experts predict that AI will shift security efforts from reactive triage to proactive threat hunting—providing additional ways to effectively protect organizations.

• Collaborative Threat Intelligence: Federated learning will allow organizations to share threat insights, creating a unified defense against global cyberattacks.

• Enhanced Prediction: Predictive models will identify attack patterns before they materialize, reducing the probability of attack and impact of breaches.

Offensive AI is evolving rapidly, enabling attackers to launch sophisticated, large-scale campaigns. The powerful countermeasure is defensive AI, which leverages advanced algorithms to anticipate, detect, and respond to these threats proactively.

At Abnormal Security, the AI detection engine exemplifies the capabilities of defensive AI. By analyzing behavioral signals at an unparalleled scale, it identifies anomalies and prioritizes high-risk events across the email environment, enabling security teams to respond faster and smarter. This proactive approach is critical in an era where the best defense is a strong offense.

See for yourself how the Abnormal defensive AI will protect your organization from cyber attacks. Schedule a demo today.