AI in Cybersecurity: Good or Bad?

As the threat landscape continues to evolve at breakneck speed, artificial intelligence (AI) has emerged as both a blessing and a potential risk in cybersecurity. Enterprise organizations are turning to AI to bolster their defenses, but threat actors are doing the same—leveraging AI to automate and scale their attacks like never before.

So, is AI ultimately good or bad for cybersecurity? The answer is more complex than a simple binary. For CISOs, Directors of IT, and cybersecurity leaders, the real question is: How can AI be implemented securely, responsibly, and effectively to protect the enterprise?

How AI Is Transforming Cybersecurity Defenses

AI and Machine Learning (ML) are fundamentally reshaping how security operations are conducted. Unlike traditional rule-based systems that rely on predefined signatures, AI algorithms can detect anomalies, correlate data from different sources, and automate incident response with unprecedented speed and scale.

Faster Threat Detection and Reduced Breach Costs:

According to IBM’s Cost of a Data Breach Report 2023, organizations that fully deployed AI and automation shortened breach lifecycles by an average of 108 days and reduced breach costs by $1.76 million compared to those without.

Scalable, Real-Time Analysis:

AI models can process billions of signals in real time—from user behavior analytics and endpoint telemetry to cloud activity logs. Vendors like Microsoft and CrowdStrike now use AI-driven behavioral detection to spot lateral movement, credential misuse, and insider threats at enterprise scale.

AI-Driven SOAR Capabilities:

Security Orchestration, Automation, and Response (SOAR) platforms integrated with AI can autonomously investigate, triage, and even remediate threats. This drastically reduces response times and reduces alert fatigue for SOC teams already facing talent shortages.

Proactive Defense:

AI-powered threat intelligence engines can forecast attack vectors, assess potential vulnerabilities, and recommend controls before attacks occur—turning threat intelligence from reactive to predictive.

But There’s a Catch: AI Can Be Weaponized

As defenders grow smarter, so do attackers. Cybercriminals are now leveraging AI to create faster, more deceptive, and highly scalable attacks. This dual-use nature of AI introduces a new class of cyber threats that are harder to detect and harder to predict.

AI-Generated Phishing & Deepfakes:

Threat actors are using generative AI to craft sophisticated phishing emails and impersonate executives using deepfake voice and video. In a notable case in early 2024, a Hong Kong finance employee was tricked into wiring $25 million during a video call composed entirely of deepfaked personas.

Polymorphic Malware:

AI allows malware to constantly change its code structure in real time—rendering it invisible to static detection engines. These polymorphic threats can bypass even advanced endpoint security solutions if not supplemented with behavioral AI.

Adversarial AI & Model Poisoning:

AI-based detection systems are themselves vulnerable. Attackers can introduce malicious data into training sets (a tactic known as “data poisoning”), skewing model outputs or introducing blind spots. A 2024 Gartner report warns that 30% of all successful attacks on AI-powered systems will involve adversarial manipulation by 2026.

Regulatory & Ethical Concerns:

Lack of explainability and transparency in AI decisions raises compliance challenges, particularly in industries with strict data governance mandates like healthcare and finance.

AI in Cybersecurity: It’s Not Good or Bad—It’s Powerful

Ultimately, AI in cybersecurity isn’t inherently good or bad. It’s a tool—a powerful one—that can be used responsibly to amplify defense, or irresponsibly (or maliciously) to cause harm.

What separates high-performing security programs from the rest is how well they govern, implement, and monitor their AI systems.

Best Practices for Responsible AI Use in Cybersecurity

• Adopt Explainable AI (XAI): Ensure your models are transparent and auditable, especially for high-stakes decisions.

• Integrate with Zero Trust Architecture: AI should support identity verification and access controls—not bypass them.

• Monitor for Model Drift and Bias: Regular evaluations are critical to ensuring model accuracy and fairness.

• Keep a Human in the Loop: AI should augment—not replace—human judgment, especially during critical incidents.

Stratejm + Bell: Secure AI, Delivered with Confidence

At Stratejm + Bell, we believe in harnessing the power of AI responsibly. Our managed cybersecurity services combine cutting-edge AI and automation with a Zero Trust-aligned architecture—delivering full-stack visibility, real-time threat detection, and scalable protection for your enterprise.

What We Offer:

• 24/7 Security Operations Center (SOC) powered by AI-driven threat detection

• Seamless integration of SOAR, behavioral analytics, and threat intelligence

• Tailored deployment of AI models with explainability and governance controls

• Expert consulting to help you scale AI securely across cloud, hybrid, and on-prem environments

Whether you’re just beginning your AI journey or expanding an existing strategy, Stratejm + Bell provides the expertise, automation, and confidence you need to stay secure in today’s evolving threat landscape.

Contact us today to get started and future-proof your cybersecurity architecture.

Sources

• IBM Security. Cost of a Data Breach Report 2023
• World Economic Forum. Global Cybersecurity Outlook 2024
• Gartner. Emerging Tech: AI in Cybersecurity, 2024
• BBC News. Deepfake Video Scam Steals $25 Million in Hong Kong, 2024
• Zscaler ThreatLabz. AI and Cloud Threat Trends, 2024