GENERATIVE AI: RISKS AND DEFENSE
Unlike traditional AI focused on classification, prediction, and pattern detection, generative AI has the ability to create entirely new content: text, images, code, audio, and even synthetic identities. According to ITware Latam, this difference is not only functional but also strategic. While classic AI acts as an intelligent sensor, generative AI can simulate human behavior, write convincing emails, generate malicious scripts, and construct false narratives. In my experience, this transforms generative AI into a tool for imitation and manipulation, not just analysis.
Generative AI not only amplifies existing threats but also introduces entirely new vectors. Platforms like WormGPT and FraudGPT allow malicious actors to automate phishing campaigns, generate voice and video deepfakes, and draft forged documents with contextual accuracy. The disruptive element isn’t the technology itself, but its accessibility: today, any attacker with access to a model can scale operations without requiring in-depth technical knowledge. From my perspective, this breaks the classic defense model, where technical sophistication was a natural barrier. Now, that barrier has disappeared.
For attackers, generative AI is a tactical advantage: it allows them to customize attacks, simulate real-time conversations, and generate convincing content in seconds. For defenders, the challenge is twofold: detecting the synthetic and validating the authentic. Traditional cybersecurity tools are not designed to identify AI-generated content, forcing the development of defensive models specifically trained to recognize generative patterns. In my experience, this requires a new layer of defense: AI that detects AI, combined with strong authentication, content traceability, and deep contextual analysis.
Today, attackers no longer need to write code or manually compose emails. Tools like WormGPT and FraudGPT allow them to generate hyper-realistic phishing attacks, malicious scripts, and fraudulent content in seconds. What once required technical expertise is now executed with simple natural language instructions. In my experience, this represents a breakthrough in the barrier to entry for cybercrime: generative AI democratizes, scales, and personalizes attacks. Companies must assume that every email, every form, and every interaction may have been fabricated by a machine trained to deceive.
Deepfakes have evolved beyond mere entertainment: today they are digital weapons. With just a few voice samples or images, attackers can clone identities, simulate video calls, and forge legal documents. Platforms like Telegram and Google Translate are being exploited to distribute this content, further complicating its detection. In my analysis, the risk lies not only in the forgery itself, but also in the trust it generates: a convincing video can disable security protocols, authorize transfers, or manipulate critical decisions.
Generative AI has taken social engineering to a new level. It’s no longer about generic emails, but about messages tailored to the victim’s psychological, professional, and emotional profile. Attackers analyze social networks, leaked emails, and behavioral patterns to construct narratives that appear legitimate. In my experience, this turns every digital interaction into a potential vector for manipulation. Defense systems must incorporate contextual and behavioral analysis, not just content filters.
One of the most underestimated risks of generative AI is its ability to fabricate digital evidence: emails, contracts, activity logs, and even screenshots. This not only affects operational security but also the legal and reputational integrity of organizations. Furthermore, AI is being used to automate disinformation campaigns, amplifying false narratives on social media and digital platforms. From my perspective, this demands a new layer of defense: algorithmic traceability, source validation, and auditing of generated content.
These malicious versions of generative models bypass ethical filters and are designed to produce offensive, fraudulent, or manipulative content. What once required technical skills is now executed with simple natural language instructions.
Key points:
They are marketed on channels like Telegram for less than €200 per month.
They generate hyper-realistic phishing, financial scripts, and malware without code.
They democratize cybercrime: any actor can launch attacks without technical expertise.
This marks a turning point. Companies must assume that every digital interaction may have been fabricated by an AI trained to deceive.
The evolution toward Offensive AI-as-a-Service is redefining the operating model of cybercriminal groups.
Key features:
Platforms offer access to models trained to simulate attacks, find vulnerabilities, and automate access.
Attacks are becoming industrialized: greater agility, precision, and anonymity.
My analysis: This forces defenders to think like attackers. Defense must be predictive, contextual, and explanatory. Reacting is no longer enough: anticipation is key.
Today’s attacks are no longer massive and generic: they are surgical, tailored to the victim’s profile.
Recent evidence:
CrowdStrike reports more than 265 groups using generative AI to automate targeted attacks.
Google warns about viruses that dynamically evolve to evade traditional protections.
Campaigns have been detected that compromise autonomous agents, digital identities, and critical systems.
Attackers not only operate using AI, but they also learn and adapt in real time. Defenses must incorporate models that detect generative patterns, validate authenticity, and correlate weak signals before they become vulnerabilities.
The proliferation of AI-generated content, from fake emails to cloned voices, has forced the development of systems capable of identifying synthetic material in real time. According to Abrecht Group and Red Seguridad, multimodal models trained to detect semantic inconsistencies, generation patterns, and visual artifacts are being integrated into monitoring platforms.
My analysis: the key is not just detecting falsehoods, but doing so before they become manipulated evidence or an operational breach. I recommend combining metadata analysis, cross-checking, and models trained on synthetic datasets to improve accuracy.
By 2025, traditional authentication will be obsolete in the face of AI-powered identity spoofing attacks. Forbes and SBS highlight the adoption of risk-based authentication (RBA), which evaluates behavior, context, and threat level in real time.
Key points:
Algorithmic validation is prioritized over static credentials.
Biometric layers, intent analysis, and tamper detection are integrated.
Authentication is no longer a filter; it’s an active defense. I recommend auditing validation algorithms regularly and applying traceability to every automated decision.
Offensive AI is evolving rapidly, and defensive models must stay ahead of the curve. According to 360 Security Group and Actu.ai, systems are being trained to identify generative patterns, synthetic sequences, and adversarial behaviors.Estrategias efectivas:
Training with simulated attack datasets.
Cross-validation with real-world generative models.
Continuous monitoring of suspicious outputs.
It’s not enough to detect anomalies; you have to understand the intent behind the content. I recommend integrating algorithmic explainability and contextual correlation to avoid false positives.
Traceability allows for the reconstruction of the origin, evolution, and purpose of every decision made by AI. TecnetOne and INISEG emphasize that explanatory systems not only justify actions but also allow for risk auditing and bias correction.Mi enfoque estratégico:
Implement detailed inference and training logs.
Use models with self-explanatory capabilities and contextual justification.
Apply legal traceability to protect digital evidence.
My analysis: explainability is not optional; it is the bridge between technical defense and ethical governance. In critical environments, I recommend prioritizing models that can be audited by humans without losing operational accuracy.
AI governance has become a critical priority. According to Kiteworks and the World Economic Forum, by 2025 organizations will face more than 50 simultaneous regulatory requirements.
Key actions:
Establish internal frameworks that regulate the use, training, and deployment of models.
Audit algorithms regularly to prevent bias, data leaks, or untraceable decisions.
Apply principles of transparency, traceability, and human oversight to autonomous systems.
Governance is not bureaucracy; it’s strategic protection. I recommend that every company define an AI committee with clear roles, risk metrics, and protocols for addressing anomalies.
Technical training can no longer be limited to traditional cybersecurity. Platforms like Udemy Business and KeepCoding have launched specific courses to train professionals in detecting AI-generated threats.
Essential content:
Prompt engineering to understand how synthetic attacks are constructed.
Algorithmic risk assessment and adversarial scenario simulation.
Use of tools such as Security Copilot, ChatGPT, and defensive models.
Teams must master both the inner workings of the models and their operational impact. I recommend hands-on training with synthetic datasets and exercises in responding to offensive AI.
Malicious automation is no longer a hypothesis. According to Palo Alto Networks and Revista Ciberseguridad, AI-powered automated attacks are increasing in speed, accuracy, and evasion.
Recommended strategies:
Simulate scenarios with offensive AI to assess real-world vulnerabilities.
Integrate predictive detection platforms that correlate weak signals.
Establish automated response protocols with human oversight.
My analysis: preparedness is not just technical; it’s organizational. I recommend that every company develop a critical scenario map and define scalable responses with integrated defensive AI.
In an environment dominated by autonomous agents, the human analyst doesn’t disappear: they transform. IBM and Hirint emphasize that the professional’s value lies in their ability to interpret, validate, and correct algorithmic decisions.
Key functions:
Monitor critical inferences in real time.
Detect biases, inconsistencies, or manipulations in generated content.
Translate technical decisions into safe operational actions.
My analysis: The analyst becomes the bridge between the synthetic and the strategic. I recommend strengthening critical thinking, contextual analysis, and algorithmic governance skills.