Understanding the Double-Edged Sword: AI in Cybersecurity

Artificial intelligence (AI) is now so prevalent that it even attends business meetings. Its impact is particularly noticeable in cybersecurity, where its unmatched data processing capabilities enhance threat detection and mitigation mechanisms.  

However, malicious actors are weaponizing these very AI capabilities. Losses from cyber incidents have quadrupled since 2017 to $2.5 billion, with AI-based attacks accelerating this rise due to their scalability and efficiency.  

Most businesses are well aware that adopting AI in some form is non-negotiable. However, given the rapid pace at which attackers are leveraging it, it is imperative to evaluate the use of AI-based defensive technologies now. Integrating new technologies isn’t just a way to combat current threats and prepare for an AI-driven world.  

The Top AI Cyber Threats to Watch 

Deepfake-Assisted Social Engineering  

Attackers are increasingly using AI-generated voice and video impersonations to execute fraudulent activities. For instance, in February 2024, a finance employee in Hong Kong was deceived into transferring $25 million after participating in a video conference where deepfake technology was used to impersonate the company’s chief financial officer and other executives.  

As deepfake scams grow more convincing, employee training alone is not enough. Instead, enterprises should invest in a defense-in-depth approach that combines stringent authentication protocols, AI-based deepfake detection tools, and verification processes for sensitive requests.  

Defining robust policies and processes, such as multi-level transaction approvals and participant identity checks in virtual meetings, can also significantly reduce risks and limit the impact of human error. 

Synthetic Media for Identity Fraud  

Though not quite as fake as deepfakes, synthetic identities are a growing issue. Using a mix of stolen and actual data, attackers create synthetic profiles that easily bypass traditional verification protocols. AI facilitates every process step- from gathering actual personal data (including social security numbers or addresses) to polishing these synthetic profiles so they resemble real individuals.  

Bad actors commonly use forged identities to obtain credit cards, secure loans, open fraudulent accounts, or commit other financial crimes. They can cause significant losses to businesses and financial institutions, regulatory penalties, and reputational damage.  

Polymorphic Malware  

AI-driven polymorphic malware like BlackMamba and EyeSpy is evolving by dynamically altering its code and attack methods to evade signature-based defenses. While AI-generated malware is still in its early stages, it may pose a threat by lowering the barrier for malicious actors to create highly adaptable and unpredictable threats. Current models may be limited, but organizations should implement AI-driven behavioral analytics and adaptive detection systems as AI development accelerates. 

Automated Reconnaissance  

AI accelerates attackers’ ability to identify vulnerabilities in legacy systems and poorly secured environments. It conducts reconnaissance at unprecedented speeds, enabling attackers to uncover and exploit weak points with minimal manual effort. For example, attackers often leverage AI-driven tools to scan entire networks and detect potential misconfigurations. Instead of focusing on specific targets, they cast a wide net, drastically increasing the likelihood of organizations being targeted. 

Data Poisoning Attacks  

Attackers compromise outputs by feeding corrupted data into AI models, leading to erroneous predictions or decisions. In critical sectors like healthcare, where AI is used for diagnostics, compromised models could lead to misdiagnosis and misdirected treatments, resulting in patient harm.  

Financial institutions are also at risk, as poisoned data can skew credit scoring, risk assessments, and fraud detection, leading to flawed financial decisions. Additionally, poisoning the data used by cybersecurity tools can impair their ability to detect genuine threats, allowing fraudulent activities or suspicious network behavior to go unnoticed and unaddressed. 

Scalable Multi-Vector Attacks  

AI enables bad actors to launch attacks across multiple entry points simultaneously. The large-scale attacks that once required well-organized hacker groups can now be executed by a few individuals exploiting weaknesses across interconnected systems. For example, a cybercriminal could simultaneously breach the network via phishing emails, inject poisoned data into an AI system, and overwhelm defenses with a distributed denial-of-service (DDoS) attack. 

Using AI to Counter Evolving Threats 

As cybercriminals leverage AI to augment their efforts, so should organizations. The following strategies highlight practical AI implementations to enhance security operations:  

AI-Powered Threat Detection  

AI excels at processing massive datasets to identify anomalies far more efficiently than human analysts. By leveraging historical data, these AI algorithms facilitate real-time detection of suspicious activities, aiding in preventing or mitigating attacks like phishing and credential stuffing.  

One of AI’s key strengths is its ability to correlate seemingly unrelated events across multiple systems and massive volumes of logs, identifying potential malicious activity that might otherwise go unnoticed. This ability to spot complex patterns and interconnections allows organizations to prioritize and address threats more effectively, improving overall security posture. 

Behavioral Analytics  

AI tools can analyze user behaviors to flag deviations signaling insider threats or compromised accounts. This capability ensures quicker identification of unusual activity, safeguarding organizations against internal vulnerabilities. Unlike threat detection, behavioral analytics focuses on continuous monitoring and zeros in how users interact with a system.  

Automated Incident Response  

Detecting AI-driven threats is just half the solution; organizations must also respond to them promptly and effectively. AI enhances existing SOAR (Security Orchestration, Automation, and Response) solutions, automating the response to detected threats and significantly reducing the damage during active breaches.  

These AI-powered systems can quickly contain incidents, ensuring a faster and more coordinated response. Organizations leveraging AI in incident response benefit from rapid containment, which reduces financial losses and minimizes downtime while improving overall operational efficiency. 

Natural Language Processing for Threat Intelligence  

Natural Language Processing (NLP)- enabled AI tools parse threat reports and online chatter to deliver real-time insights into emerging threats. These tools enhance situational awareness by prioritizing high-risk alerts, allowing security teams to allocate resources effectively. Risk prioritization is crucial when IT and security teams are overwhelmed with alerts, some of which are false positives and don’t require any action.  

AI-Enhanced Red Teaming  

Red teams simulate advanced and adaptive attacks using AI, improving an organization’s ability to identify and mitigate vulnerabilities proactively. These simulations mimic sophisticated adversary tactics, providing deeper insights into potential weaknesses and remediation pathways. They offer another (much-needed) layer of security, helping to identify weaknesses that traditional pen tests may miss. 

Practical Steps to Integrate AI into Cybersecurity 

Adopting AI-driven cybersecurity solutions requires careful planning and execution. Businesses must approach this process with clear objectives and a structured strategy. Here are some steps that can help you get started:  

1. Start Small– Focus on specific use cases like anomaly detection or predictive analytics to demonstrate AI’s value. Initial projects should deliver quick wins, building organizational confidence in AI’s capabilities while mitigating the implementation complexity. 

2. Establish and Update Strong Policies and Governance—Enforce data privacy protections and align practices with compliance frameworks to ensure the responsible use of AI. Key frameworks include ISO/IEC 42001, the latest standard for AI management systems, which provides a framework for managing AI throughout its lifecycle. Ensure you review existing policies so they cover technical and procedural controls around the safe use of AI.  

3. Evaluate and Vet Vendors– ​​Assess vendors to verify their proven experience and success deploying AI solutions. Be cautious of marketing claims like “AI-powered,” and take the time to assess how AI is implemented. Understand the data processing methods, storage practices, and associated privacy implications.  

4. Ensure Human Oversight—While AI replaces humans in some tasks—particularly when it comes to automation—human oversight and judgment are still essential to validate decisions and guard against adversarial AI risks. Ensure your team has the right know-how or access to third-party expertise to make informed decisions.  

5. Measure ROI—Track measurable benefits, such as reduced breach occurrences or faster incident response times, to validate AI investments. Highlighting tangible improvements builds stakeholder trust and secures ongoing support for AI-driven cybersecurity initiatives. 

The Road Ahead: Balancing AI’s Dual Nature 

AI is both the devil and the angel advocate in cybersecurity, and acknowledging this is the first step in ensuring its safe usage. As much as AI-based attacks are evolving at unprecedented speed, and companies may feel coerced to match this speed, it’s crucial to take some time to review exactly where and how you should implement AI in your organization. Equally important is to choose partners that understand your needs and are equipped with the right tools.  

Richey May helps you incorporate defenses against AI-driven threats into a broader, robust cybersecurity strategy. While protecting against AI-driven attacks is crucial, it’s only one aspect of overall security. We focus on integrating AI-specific defenses with a comprehensive approach that includes multi-layered security tools, employee training, and expert guidance.  

Embedding AI security into your cybersecurity framework helps you achieve long-term resilience and maintain an adaptable and robust security posture across the business. Learn more about how Richey May can augment your AI and broader security capabilities.