Emerging AI Threats To Navigate In 2025 And Beyond
12 February, 2025 / Articles
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As society integrates AI into sectors such as healthcare, finance, manufacturing and transport, the potential for catastrophic blowback grows if these autonomous bytes are not properly regulated and monitored. Listed below are some heads-up threats to be aware of as organizations prepare their risk management plans for 2025.
1. Overreliance On Agentic AI Introducing New Risks
Agentic AI systems are AI agents that aren’t just responding to prompts or generating content—they’re making decisions or executing complex tasks without human oversight (think autonomous vehicles). Since these systems have excessive agency and possess deep access to data, code and functions, they will be a hot target for malicious threat actors.
2. Attackers Exploiting AI’s Logical Weaknesses
Attackers may induce undesirable behaviors or create malicious code and outputs by corrupting AI’s training data or manipulating its algorithms. Threat actors can embed backdoors or discover ways to circumvent AI-based protections such as automated fraud detection.
3. Shadow AI Bypassing Established Security Protocols
Shadow AI is where employees deploy AI tools without organizational approval or oversight. This practice can bypass established security protocols, creating blind spots in an organization’s defenses and introducing unmonitored vulnerabilities.
4. LLMs Being Weaponized And Automated
Hackers may weaponize, abuse or jailbreak large language models (LLMs) to automate phishing campaigns and generate convincing messages at scale without manual input. Bad actors will simulate customer service chats, gaining sensitive information under the guise of providing legitimate support.
5. Rushed Integration Of AI Systems Introducing New Vulnerabilities
A rush to integrate AI systems without rigorous testing may result in unforeseen vulnerabilities. AI systems can generate “hallucinations” (outputs based on plausible but incorrect datasets), which could lead to flawed decision-making.
6. An Emerging Battleground Of Competing AI Systems
As both attackers and defenders deploy AI, the cybersecurity landscape will evolve into a battleground of competing AI systems. Attackers might deploy “adversarial AI,” a technique where AI is used to hunt vulnerabilities in security defenses and even bypass certain security measures.
7. Exploding Deepfake Phishing Attacks
Deepfake tools enable attackers to create realistic audio and video simulations, which can be exploited for more sophisticated, persuasive and targeted phishing attacks. They will synthesize voices and identities to convincingly impersonate individuals and conduct financial fraud or smear campaigns. The growing sophistication of live deepfakes will complicate detection efforts and enable bad actors to bypass identity verification checks.
8. Bigger Wave Of Threat Actors Faking Data Breaches
Generative AI tools will be used to create realistic-looking fake datasets to sell to other bad actors or to launch a fake data breach. By faking data breaches, cybercriminals can create a distraction and leverage the guise of a data breach to uncover a company’s security infrastructure, capabilities, processes and response times.
9. Challenges With Ethics, Compliance And Accountability
Threat actors can misuse AI to conduct unauthorized surveillance or harvest private data, violating data privacy regulations. They can intentionally introduce biases in datasets leading to discriminatory outcomes. They can leverage AI’s opacity to evade responsibility for their malicious actions, further complicating legal and regulatory enforcement.
How Organizations Can Mitigate Emerging AI Threats
To help mitigate AI threats, organizations can elevate certain best practices.
• Adopt frameworks like MITRE ATLAS to better understand the threat landscape of AI-driven systems and to implement a structured approach to identify, mitigate and counteract adversarial tactics targeting AI technologies.
• Invest in holistic security systems such as single-vendor SASE that can provide end-to-end visibility into networks, users and devices, as well as recognize subtle changes or anomalies in network behavior.
• To counter adversarial AI, consider relying on adaptive AI algorithms that can dynamically respond to emerging threats and strengthen their own defenses by learning from each interaction.
• Implement robust AI governance policies to curb shadow AI. Converged security tools like SASE can provide insight into applications and network traffic, enabling the detection of unauthorized data sharing and blocking employees’ use of potentially harmful shadow AI applications.
• Integrate real-time threat intelligence into organizational defenses to quickly adapt to new AI-fueled attack patterns.
• Engage active human oversight, review and judgment over AI-generated decisions, processes and insights.
• Educate staff and stakeholders about the dangers of AI technology, and train them to scrutinize unusual requests, increase vigilance and recognize telltale signs of deepfakes and misinformation pedaling.
• Conduct frequent red-teaming exercises to test defenses against attacks on AI infrastructure and other LLM-fueled attacks.
In 2025, expect the rapid advancement of AI to bring forth opportunities as well as significant cybersecurity risks. From agentic AI systems acting autonomously to the rise of sophisticated AI-driven cyberattacks, the threat landscape is evolving faster than most organizations can keep pace.
To stay ahead of these risks, organizations must prioritize security measures—investing in AI education, human testing and monitoring of AI systems; implementing converged security and networking models that can co-relate security events across the IT environment; and following best practices specific to AI challenges. Only through a forward-thinking, unified approach can we hope to mitigate the dangers AI poses while harnessing its transformative potential.
