Decoding AI Security Risks and Discerning AI Hype vs. Reality

January 13, 2025

AI attacks are reshaping the cybersecurity field, introducing new risks and challenges alongside existing vulnerabilities. While traditional threats like SQL injections and broken access controls persist, AI-powered attacks demand a more nuanced and adaptive approach. From amplified distributed denial-of-service (DDoS) attacks to deepfake social engineering and AI-specific zero-day exploits, these challenges highlight the critical need for resilient security strategies.

This blog explores three critical AI security risks and provides insights into navigating these emerging challenges effectively.

AI-Powered DDoS Bot Attacks

Bad actors are leveraging AI to amplify the scale and sophistication of DDoS attacks. By mimicking legitimate user behavior, these AI-enhanced attacks make it increasingly difficult for conventional defenses to distinguish malicious activity. According to Nokia’s 10th Threat Intelligence Report, DDoS attacks monitored between June 2023 and June 2024 surged from sporadic occurrences to over 100 attacks daily in many networks. Traditional defenses, such as firewalls and basic intrusion detection systems, often fall short against these adaptive threats.

To address this, organizations are turning to advanced endpoint detection response (EDR) and managed detection response (MDR) solutions, powered by AI/ML capabilities. These tools enhance visibility into network traffic, enabling business to stay ahead of increasingly agile adversaries without sacrificing efficiency.

Deepfake Detection and Other Social Engineering Scams

Deepfakes, powered by generative adversarial networks (GANs) and advances synthesis technologies, represent a growing threat to identity verification processes. These hyper-realistic forgeries can be used to impersonate trusted individuals, bypassing voice, image or video-based verification security measures with alarming ease.

As these tools become more accessible, organizations must adopt proactive measures to safeguard against deepfake-driven attacks. This includes conducting regular vulnerability assessments, investing in emerging detection technologies, and educating employees about identifying potential scams. Recognizing the limitations of current tools is essential to staying resilient in the face of rapidly evolving AI capabilities.

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Source: ISC2, AI in Cyber 2024: Is the Cybersecurity Profession Ready?

AI-Specific Zero-Day Cybersecurity Vulnerabilities

Zero-day vulnerabilities – flaws unknown to application or API developers – are a longstanding concern in cybersecurity. However, AI introduces a new layer of complexity. Systems designed to learn and adapt to benefit business processes can inadvertently become targets themselves, exposing sensitive information or enabling adversaries to manipulate (or poison) proprietary models. models.

Key vulnerabilities include:

Beyond the immediate impact, such vulnerabilities can also result in data and model poisoning, where malicious inputs degrade system accuracy or reliability. For instance, bad actors may reverse engineer proprietary AI models, uncovering their inner workings to gain unauthorized access or train countermeasures. Data and model poisoning will have a further ripple effect, leading to the widespread dissemination of flawed or malicious outputs.

Mitigating these risks requires rigorous testing, continuous monitoring and collaboration across development, operations and security teams.

Augment Your Cybersecurity Strategy with AI-Specific Directives

Many organizations find themselves in a reactive posture, addressing threats as they emerge. However, AI-driven attacks demand a shift toward proactive, comprehensive AI security strategies. Incorporating AI-specific directives into broader governance frameworks enables businesses to mitigate risks while fostering innovation.

Start by:

While adopting such a robust AI security policy may be at the top of the list of priorities, the reality is that many organizations lack the AI expertise and resources to craft company-wide policies for all relevant use cases. According to a recent ISACA study, only 10% of organizations have formal, comprehensive policies in place for generative AI. This gap underscores the need for decisive action.

By defining and aligning AI-specific security directives with overall business goals and existing cybersecurity measures, organizations can better position themselves to combat AI-specific cybersecurity risks. A sound AI security strategy not only addresses emerging threats but also ensures that innovation thrives in a secure and controlled environment.

Get started by downloading our free AI security field guide.