AI and Cybersecurity: A Double-Edged Sword for Businesses

The same technology that lets a security team detect anomalous patterns across millions of network events in seconds also lets an attacker generate thousands of personalized, grammatically perfect phishing emails in that same time. Artificial intelligence hasn't picked a side in cybersecurity — it's accelerating both defensive and offensive capabilities, and understanding both sides of that equation is increasingly necessary for any modern security strategy.

Phishing generated with unprecedented quality and personalization, eliminating traditional signals like poor writing or grammar mistakes that used to help identify phishing attempts, and enabling personalization at scale that used to require extensive manual research per target.

Voice and image deepfakes growing increasingly accessible and convincing, a risk we cover in detail in our guide on financial deepfakes, lowering the technical barrier that used to limit this type of attack to actors with considerable resources.

Automated reconnaissance and vulnerability discovery, where AI-based tools can scan and analyze target systems much faster than traditional manual methods, accelerating the research phase that used to take an attacker days or weeks.

Polymorphic malware generation, malicious code that automatically modifies itself to evade traditional signature-based detection, undermining the effectiveness of antivirus solutions relying solely on known pattern recognition.

Anomaly detection at a scale impossible for human analysis, identifying subtle suspicious behavior patterns across data volumes no human team could manually review in real time.

Faster automated response, where AI-based systems can identify and contain threats in seconds, instead of relying exclusively on human intervention that introduces natural delays.

Predictive threat analysis, using historical patterns to anticipate likely attack types before they happen, instead of reacting only after the fact.

Automating repetitive security tasks, freeing up human analysts to focus on more complex investigation and strategic decision-making, instead of low-value manual triage tasks.

It's tempting to think that if attackers have better AI tools, defenders simply need better AI tools to compensate. The reality is more nuanced: the advantage tends to favor whoever adopts the technology fastest and integrates it most fully into their overall process, not just whoever has access to the most sophisticated tool in isolation.

Beyond AI-powered external threats, businesses adopting artificial intelligence tools internally face their own risks — from accidental exposure of sensitive information when using generative AI tools without proper controls, to dependence on AI systems whose decisions aren't always fully transparent or auditable.

Update threat recognition training to reflect that traditional phishing signals (poor writing, obvious errors) are no longer as reliable as before, a topic we go deeper on in our guide on how to spot a phishing email.

Actively evaluate AI-powered security solutions for detection and response, since traditional tools relying solely on known signatures face growing limitations against faster-evolving threats.

Establish clear policies on generative AI tool use within the business, avoiding accidental exposure of sensitive information through use without appropriate controls.

Keep the human element in critical decisions, since AI boosts detection and response capability, but higher-impact decisions still benefit from expert human judgment.

Gartner points to four critical threats for security leaders in 2026–2027: deepfakes, compromised AI applications, prompt injection and software supply-chain attacks. IBM found that one in four malicious breaches already involved attacker-side AI. Go deeper on each front: 10 AI risks for businesses, voice deepfakes and CEO fraud and, to know where your company stands, the AI audit.

Spain's INCIBE handled more than 122,000 incidents in 2025, 26 % more than the previous year, and in June 2026 published guidance on frontier AI models: phishing-resistant MFA, Zero Trust, strict privilege control, deepfake awareness training and supervision of AI-generated code. On the regulatory side, the EU AI Act applies in phases:

In Spain there is no national AI law in force yet: the Council of Ministers approved the organic bill on the good use and governance of AI in May 2026 and sent it to Congress. The supervisory authority is AESIA (the Spanish AI Supervision Agency, based in A Coruña), and the AEPD remains in charge when AI processes personal data. Fines under the regulation reach EUR 35 million or 7 % of worldwide turnover.