Artificial Intelligence (AI) and AI-agents: A Game-Changer for Both Cybersecurity and Cybercrime (By Anna Collard)

 

By Anna Collard, SVP Content Strategy&Evangelist KnowBe4 Africa (www.KnowBe4.com).

Artificial Intelligence is no longer just a tool—it is a gamechanger in our lives, our work as well as in both cybersecurity and cybercrime. While businesses leverage AI to enhance defences, cybercriminals are weaponising AI to make these attacks more scalable and convincing​.

In 2025, researchers forecast that AI agents, or autonomous AI-driven systems capable of performing complex tasks with minimal human input, are revolutionising both cyberattacks and cybersecurity defences. While AI-powered chatbots have been around for a while, AI agents go beyond simple assistants, functioning as self-learning digital operatives that plan, execute, and adapt in real time. These advancements don’t just enhance cybercriminal tactics—they may fundamentally change the cybersecurity battlefield.

How Cybercriminals Are Weaponising AI: The New Threat Landscape

AI is transforming cybercrime, making attacks more scalable, efficient, and accessible. The WEF Artificial Intelligence and Cybersecurity Report (2025)  (https://apo-opa.co/3QO7O7H) highlights how AI has democratised cyber threats, enabling attackers to automate social engineering, expand phishing campaigns, and develop AI-driven malware​. Similarly, the Orange Cyberdefense Security Navigator 2025 (https://apo-opa.co/3FfJZ6c) warns of AI-powered cyber extortion, deepfake fraud, and adversarial AI techniques. And the 2025 State of Malware Report by Malwarebytes (https://apo-opa.co/43lwZpY) notes, while GenAI has enhanced cybercrime efficiency, it hasn’t yet introduced entirely new attack methods—attackers still rely on phishing, social engineering, and cyber extortion, now amplified by AI. However, this is set to change with the rise of AI agents—autonomous AI systems capable of planning, acting, and executing complex tasks—posing major implications for the future of cybercrime.

Here is a list of common (ab)use cases of AI by cybercriminals:

AI-Generated Phishing&Social Engineering

Generative AI and large language models (LLMs) enable cybercriminals to craft more believable and sophisticated phishing emails in multiple languages—without the usual red flags like poor grammar or spelling mistakes. AI-driven spear phishing now allows criminals to personalise scams at scale, automatically adjusting messages based on a target’s online activity. AI-powered Business Email Compromise (BEC) scams are increasing, as attackers use AI-generated phishing emails sent from compromised internal accounts to enhance credibility​. AI also automates the creation of fake phishing websites, watering hole attacks and chatbot scams, which are sold as AI-powered crimeware as a service’ offerings, further lowering the barrier to entry for cybercrime​.

Deepfake-Enhanced Fraud&Impersonation

Deepfake audio and video scams are being used to impersonate business executives, co-workers or family members to manipulate victims into transferring money or revealing sensitive data. The most famous 2024 incident was UK based engineering firm Arup (https://apo-opa.co/4h56I27) that lost $25 million after one of their Hong Kong based employees was tricked by deepfake executives in a video call. Attackers are also using deepfake voice technology to impersonate distressed relatives or executives, demanding urgent financial transactions.

Cognitive Attacks

Online manipulation—as defined by Susser et al. (2018) (https://apo-opa.co/4h8qxpw) —is “at its core, hidden influence — the covert subversion of another person’s decision-making power”. AI-driven cognitive attacks are rapidly expanding the scope of online manipulation, leveraging digital platforms and state-sponsored actors increasingly use generative AI to craft hyper-realistic fake content, subtly shaping public perception while evading detection. These tactics are deployed to influence elections, spread disinformation, and erode trust in democratic institutions. Unlike conventional cyberattacks, cognitive attacks don’t just compromise systems—they manipulate minds, subtly steering behaviours and beliefs over time without the target’s awareness. The integration of AI into disinformation campaigns dramatically increases the scale and precision of these threats, making them harder to detect and counter.

The Security Risks of LLM Adoption

Beyond misuse by threat actors, business adoption of AI-chatbots and LLMs introduces their own significant security risks—especially when untested AI interfaces connect the open internet to critical backend systems or sensitive data. Poorly integrated AI systems can be exploited by adversaries and enable new attack vectors, including prompt injection, content evasion, and denial-of-service attacks. Multimodal AI expands these risks further, allowing hidden malicious commands in images or audio to manipulate outputs.

Additionally, bias within LLMs poses another challenge, as these models learn from vast datasets that may contain skewed, outdated, or harmful biases. This can lead to misleading outputs, discriminatory decision-making, or security misjudgments, potentially exacerbating vulnerabilities rather than mitigating them. As LLM adoption grows, rigorous security testing, bias auditing, and risk assessment are essential to prevent exploitation and ensure trustworthy, unbiased AI-driven decision-making.

When AI Goes Rogue: The Dangers of Autonomous Agents

With AI systems now capable of self-replication, as demonstrated in a recent study (https://apo-opa.co/4i7HgdN), the risk of uncontrolled AI propagation or rogue AI—AI systems that act against the interests of their creators, users, or humanity at large – is growing. Security and AI researchers have raised concerns that these rogue systems can arise either accidentally or maliciously, particularly when autonomous AI agents are granted access to data, APIs, and external integrations. The broader an AI’s reach through integrations and automation, the greater the potential threat of it going rogue, making robust oversight, security measures, and ethical AI governance essential in mitigating these risks.

The future of AI Agents for Automation in Cybercrime

A more disruptive shift in cybercrime can and will come from AI Agents, which transform AI from a passive assistant into an autonomous actor capable of planning and executing complex attacks. Google, Amazon, Meta, Microsoft, and Salesforce are already developing Agentic AI for business use, but in the hands of cybercriminals, its implications are alarming. These AI agents can be used to autonomously scan for vulnerabilities, exploit security weaknesses, and execute cyberattacks at scale. They can also allow attackers to scrape massive amounts of personal data from social media platforms and automatically compose and send fake executive requests to employees or analyse divorce records across multiple countries to identify individuals for AI-driven romance scams, orchestrated by an AI agent. These AI-driven fraud tactics don’t just scale attacks—they make them more personalised and harder to detect. Unlike current GenAI threats, Agentic AI has the potential to automate entire cybercrime operations, significantly amplifying the risk​.

How Defenders Can Use AI&AI Agents

Organisations cannot afford to remain passive in the face of AI-driven threats and security professionals need to remain abreast of the latest development. Here are some of the opportunities in using AI to defend against AI:

AI-Powered Threat Detection and Response:

Security teams can deploy AI and AI-agents to monitor networks in real time, identify anomalies, and respond to threats faster than human analysts can. AI-driven security platforms can automatically correlate vast amounts of data to detect subtle attack patterns that might otherwise go unnoticed, create dynamic threat modelling, real-time network behaviour analysis, and deep anomaly detection​. For example, as outlined by researchers of Orange Cyber Defense (https://apo-opa.co/3FfJZ6c), AI-assisted threat detection is crucial as attackers  increasingly use “Living off the Land” (LOL) techniques that mimic normal user behaviour, making it harder for detection teams to separate real threats from benign activity. By analysing repetitive requests and unusual traffic patterns, AI-driven systems can quickly identify anomalies and trigger real-time alerts, allowing for faster defensive responses.

However, despite the potential of AI-agents, human analysts still remain critical, as their intuition and adaptability are essential for recognising nuanced attack patterns and leverage real incident and organisational insights to prioritise resources effectively.

Automated Phishing and Fraud Prevention:

AI-powered email security solutions can analyse linguistic patterns, and metadata to identify AI-generated phishing attempts before they reach employees, by analysing writing patterns and behavioural anomalies. AI can also flag unusual sender behaviour and improve detection of BEC attacks​. Similarly, detection algorithms can help verify the authenticity of communications and prevent impersonation scams. AI-powered biometric and audio analysis tools detect deepfake media by identifying voice and video inconsistencies. *However, real-time deepfake detection remains a challenge, as technology continues to evolve.

User Education&AI-Powered Security Awareness Training:

AI-powered platforms (e.g., KnowBe4’s AIDA) deliver personalised security awareness training, simulating AI-generated attacks to educate users on evolving threats, helping train employees to recognise deceptive AI-generated content​ and strengthen their individual susceptility factors and vulnerabilities.

Adversarial AI Countermeasures:

Just as cybercriminals use AI to bypass security, defenders can employ adversarial AI techniques, for example deploying deception technologies—such as AI-generated honeypots—to mislead and track attackers, as well as continuously training defensive AI models to recognise and counteract evolving attack patterns.

Using AI to Fight AI-Driven Misinformation and Scams:

AI-powered tools can detect synthetic text and deepfake misinformation, assisting fact-checking and source validation. Fraud detection models can analyse news sources, financial transactions, and AI-generated media to flag manipulation attempts​. Counter-attacks, like shown by research project Countercloud (https://apo-opa.co/3Xp1RSs) or O2 Telecoms AI agent “Daisy” (https://apo-opa.co/4h15eGp) show how AI based bots and deepfake real-time voice chatbots can be used to counter disinformation campaigns as well as scammers by engaging them in endless conversations to waste their time and reducing their ability to target real victims​.

In a future where both attackers and defenders use AI, defenders need to be aware of how adversarial AI operates and how AI can be used to defend against their attacks. In this fast-paced environment, organisations need to guard against their greatest enemy: their own complacency, while at the same time considering AI-driven security solutions thoughtfully and deliberately. Rather than rushing to adopt the next shiny AI security tool, decision makers should carefully evaluate AI-powered defences to ensure they match the sophistication of emerging AI threats. Hastily deploying AI without strategic risk assessment could introduce new vulnerabilities, making a mindful, measured approach essential in securing the future of cybersecurity.

To stay ahead in this AI-powered digital arms race, organisations should:

✅Monitor both the threat and AI landscape to stay abreast of latest developments on both sides.

✅ Train employees frequently on latest AI-driven threats, including deepfakes and AI-generated phishing.
✅ Deploy AI for proactive cyber defense, including threat intelligence and incident response.
✅ Continuously test your own AI models against adversarial attacks to ensure resilience.

Distributed by APO Group on behalf of KnowBe4.