Generative AI is transforming how security teams detect threats and respond to incidents, but the same technology that helps defenders also helps criminals launch more convincing attacks at scale. The key to managing this dual-use risk is understanding where AI adds real value and where human verification remains non-negotiable.

How Is Generative AI Actually Being Used in Cybersecurity?

Generative AI, which includes large language models (LLMs) like ChatGPT and Claude, is being deployed across multiple security operations to compress the time analysts spend on routine tasks. Rather than replacing human judgment, these systems handle the clerical work that sits between a security analyst and a decision.

Security teams are using generative AI in several practical ways:

• Alert Triage and Prioritization: An LLM can read hundreds of raw security alerts, cluster related ones, remove duplicates, and return a ranked shortlist with a one-line explanation for each item. An analyst who would normally spend hours reading 400 alerts can now review 12 prioritized decisions instead.
• Detection Rule Writing: Security engineers can describe a suspicious behavior in plain language and receive a draft detection rule, regex pattern, or query for their security information and event management (SIEM) system. This turns a half-day of manual rule tuning into an afternoon of review and refinement.
• Incident Response and Timeline Reconstruction: During an active incident, generative AI can pull together scattered logs and draft a timeline, scope assessment, and initial root-cause narrative while responders focus on the live problem. The model also translates technical findings into executive summaries or regulator-ready reports, work that typically consumes a responder's evening.
• Threat Intelligence Summarization: Security teams drown in new advisories and vulnerability disclosures. A generative model can condense a 30-page report into the five most critical points, helping analysts absorb threat intelligence faster than humanly possible.

The honest assessment is that generative AI is a force multiplier for both defenders and attackers. It collapses the time analysts spend reading and writing, turning a noisy alert queue into a ranked shortlist and scattered logs into a draft investigation a human can verify.

What Are the Real Risks of Relying on AI for Security Decisions?

The risks are specific and manageable, but they are real. Generative AI can produce plausible-sounding explanations that are factually wrong, a problem known as hallucination. A model might confidently explain a security alert in a way that sounds authoritative but misses the actual threat or invents details that do not exist in the underlying data.

Other risks include poisoned training data, sensitive information accidentally sent to a third-party AI model, and analysts who stop verifying AI output because the explanations sound so convincing. Each of these risks has a control, and the control is almost always keeping a human in the loop.

The core issue is that generative AI is only as trustworthy as the data and context behind it. If a model receives incomplete or misleading information, it will produce a confident but incorrect answer. This is why security teams must ground AI-driven workflows in real, full-stack cloud context rather than allowing the model to make educated guesses based on partial information.

How Can Security Teams Safely Adopt Generative AI?

• Secure the AI Pipeline: Protect the models, data, and systems that feed generative AI. Ensure that sensitive security logs and customer data are not sent to public AI services without encryption and proper data handling agreements.
• Establish Governance and Guardrails: Define clear policies for when AI output should be trusted and when it must be verified. Not all AI recommendations carry the same risk; a ranked alert list requires human review, while a draft report can be edited more freely.
• Maintain Human-in-the-Loop Oversight: Require analysts to verify AI findings before acting on them. The model's job is to compress reading and writing time, not to replace human judgment. An analyst who understands the underlying data can spot when a model has hallucinated or missed context.
• Ground AI in Real Context: Feed full-stack, real-time cloud environment data into AI workflows so the answers analysts receive are based on actual infrastructure, not plausible guesses. This dramatically reduces hallucination and improves the reliability of AI-generated findings.

Why Are Attackers Also Weaponizing Generative AI?

The same technology that helps defenders also lowers the skill and time an attacker needs to run a convincing campaign. Phishing and social engineering attacks that once required significant effort and language expertise can now be produced at scale by attackers with minimal technical skill.

An attacker can use generative AI to produce a flawless spear-phishing email in a language they do not speak, craft convincing pretexting scripts, or generate deepfake videos that impersonate trusted figures. This is why the framing of generative AI in security is not "good or bad." It changes both sides of the fight at once.

The broader context is that deepfakes have become one of the most effective tactics in a cybercriminal's playbook. Deloitte predicts that generative AI could drive fraud losses to $40 billion in the United States alone by 2027. A global survey of 7,000 consumers found that deepfake scams ranked as the top security concern, and consumers correctly identify high-quality deepfakes only 24.5 percent of the time.

What Tools Are Emerging to Detect AI-Generated Threats?

As deepfakes proliferate across social media at an unprecedented pace, new detection tools are emerging to help consumers and organizations separate fact from fabrication. Bitdefender recently launched RealCheck, a standalone solution that assesses both the likelihood that a video has been manipulated and whether it carries malicious intent, such as financial fraud, credential theft, or defamation.

RealCheck conducts a multi-layered analysis that recognizes not all synthetic or altered videos are malicious; some are clearly satirical or entertainment-driven. The tool delivers a detailed report covering manipulation likelihood, deceptive intent, and transcript-level indicators, rather than a simple yes-or-no verdict.

"Deepfakes that were once easy to spot have become nearly impossible to detect with the naked eye. Bitdefender RealCheck closes that gap, delivering powerful intelligence on whether a video has been manipulated and whether it was designed with malicious intent," said Ciprian Istrate, senior vice president of operations, Consumer Solutions Group at Bitdefender.

Ciprian Istrate, Senior Vice President of Operations, Consumer Solutions Group at Bitdefender

The tool validates deepfakes across all major social media platforms, including X, YouTube, Instagram, Facebook, and TikTok, and identifies public figures, including celebrities, business executives, and politicians, who are currently being impersonated in active deepfake campaigns. Analysis is shareable, so users can send verified findings to family and friends even if they do not have an account, creating a meaningful line of defense against deepfakes that spread through group chats.

What Does This Mean for Organizations and Individuals?

The emergence of generative AI in both security operations and attack campaigns means that organizations must invest in AI security fundamentals before deploying AI-driven tools. Teams that benefit most are those that are understaffed and drowning in context-switching; generative AI removes the clerical work that sits between an analyst and a decision, freeing them to focus on judgment calls that require human expertise.

For individuals, the proliferation of deepfakes and AI-generated scams means that skepticism and verification are becoming essential digital hygiene. Social media has now surpassed every other channel as the leading medium for successful scams, and consumers are far more likely to fall for high-quality deepfakes than they realize.

The bottom line is that generative AI is neither a silver bullet for security nor a doomsday weapon. It is a tool that amplifies both human and adversarial capabilities. Security teams that understand its strengths and limitations, maintain human oversight, and ground AI decisions in real context will extract significant value. Those that treat AI output as gospel risk making costly mistakes.