In 2023, JPMorgan Chase faced a sophisticated phishing attack that nearly compromised client data worth millions. The attack wasn’t typical—it used AI-generated content to craft emails and messages indistinguishable from legitimate communications, fooling even the most cautious employees.

This incident highlighted the cybersecurity risks that come with generative AI, whose capability to create lifelike outputs can unwittingly expose sensitive data and help cybercriminals develop new methods of attack. As more organizations turn to AI to drive efficiencies and insights, understanding and addressing these risks is critical to maintaining security, trust, and compliance.

This article explores the main cybersecurity risks posed by generative AI and outlines practical strategies to mitigate them effectively.

Understanding Generative AI Cybersecurity Risks

Generative AI, like language models and image-generation tools, can create anything from lifelike images to entire datasets by learning from existing information. This capability brings powerful advantages in automation and efficiency, but it also opens up some serious security risks that companies need to understand.

Here’s a look at the key cybersecurity risks generative AI introduces:

1. Expanded Points of Entry: When generative AI is added to a company’s systems, it increases the number of possible entry points for attackers. Every time AI interacts with outside data, there’s a chance for threats to slip through. Left unchecked, these interactions can expose private information or let in malware.
2. Risk of Sensitive Data Exposure: AI models trained on sensitive data can unintentionally store and later reveal private information. Without the right data protections in place, a model might accidentally disclose confidential details, which poses a huge risk for companies that rely on secure data.
3. Creating Misleading or Harmful Content: Generative AI is powerful enough to create realistic-looking but false information, and attackers can use it to spread fake reports, news, or even create synthetic identities for fraud. This misinformation can lead to confusion, financial loss, or worse.

These risks show that while generative AI offers exciting possibilities, it’s crucial to have safeguards in place to prevent these new vulnerabilities from becoming real threats.

Mitigation Strategies Generative AI Cybersecurity Risks

Effectively managing the risks tied to generative AI requires proactive steps and a focus on security at every stage of deployment. Here are five key strategies to help organizations protect against AI-driven threats:

• Protect Sensitive Inputs with Data Sanitization: Data sanitization goes beyond removing personal details; it involves techniques like differential privacy, which adds a small amount of noise to data to prevent models from retaining specific information. This way, models learn patterns without storing sensitive details.
  • How to Implement: Use tools for data anonymization and apply secure multi-party computation to combine data safely from various sources without risking exposure.
• Strengthen Data Security with Advanced Encryption: Encryption remains a primary defense for data security, but as AI evolves, encryption methods must keep pace. Consider quantum-resistant encryption for stronger protection against future threats, and adopt automated key management systems that rotate encryption keys frequently.
  • How to Implement: Look into quantum-safe encryption standards and use key rotation protocols to limit exposure if data is compromised.
• Build Security into AI Models with DevSecOps: Security shouldn’t just be added after development—it should be integrated at every stage. DevSecOps frameworks incorporate security into development, with "red teams" (internal groups that simulate attacks) testing models before release.
  • How to Implement: Introduce regular vulnerability assessments and set up a DevSecOps workflow where security checks are part of the entire development process.
• Increase Transparency with Explainable AI: Explainable AI helps monitor what models are doing and why, making it easier to catch unusual behavior. Layered explainability provides insights for both technical and non-technical users, giving a clearer view of AI decisions.
  • How to Implement: Pair explainable AI tools with anomaly detection so any unexpected outputs get flagged for review.
• Establish a Governance Framework: AI Governance frameworks set clear rules for AI use across departments. Regular updates ensure guidelines stay relevant as technology and compliance needs change.
  • How to Implement: Create a cross-departmental governance team responsible for updating policies and ensuring all AI use meets current security and compliance standards.
• Employ Ema: With Ema, you gain a dedicated, compliant, and transparent AI partner, designed to safeguard your systems and keep your operations secure and reliable.

How to Implement: EmaFusion™ combines over 100 specialized language models, delivering high-accuracy results while reducing AI "hallucinations" and securing data integrity. Meanwhile, the Generative Workflow Engine™ orchestrates AI agents to streamline complex tasks, adapting intelligently to your organization's needs.

Taking these steps helps build a resilient approach to AI security, making it possible to leverage AI’s capabilities while keeping data and systems safe.

To complete a robust defense, training employees to recognize and respond to AI-driven threats is just as important. Let’s look at how employee awareness can be a strong line of defense against cybersecurity risks.

Building a Multi-Layered Defense Against AI Cybersecurity Risks: The Role of Training, Frameworks, and Collaboration

As generative AI technology becomes more advanced, the complexity of managing its risks also increases. Here’s why each of the following sections is key to creating a well-rounded defense:

• Employee Awareness and Training
  Often, employees are the first to encounter AI-driven threats, such as phishing emails or suspicious data requests. By equipping them with the skills to recognize these risks, organizations strengthen their frontline defenses.
• Implementing Security Frameworks
  Establishing strong security frameworks ensures that protective measures are embedded throughout the organization. Frameworks like Zero Trust, SASE, and DLP create structured, adaptive defenses that are vital for securing AI systems across departments.
• Collaborative Cyber Defense
  Since AI-related threats are evolving quickly, collaborating with industry networks and cybersecurity experts can enhance an organization’s ability to anticipate and counter these threats. Collective knowledge and shared resources give companies insights into the latest AI-driven attacks and best practices.

Let’s dive into practical strategies for each of these pillars, showing how companies can integrate training, security frameworks, and collaboration to better protect themselves against generative AI’s cybersecurity risks.

Enhancing Employee Awareness and Training

Employees are often the first line of defense against cybersecurity threats, especially those posed by generative AI workflows. Empowering your team with the right knowledge and skills builds a strong barrier against evolving threats. Here's how organizations can effectively train employees to recognize and respond to AI-driven risks:

Educating Employees on AI-Specific Data Sharing Boundaries

Generative AI tools are powerful, but not all data is safe for input. Employees need clear guidelines on what information should never be shared with AI systems.

How can you strategize against it?

• To ensure retention, companies can use a layered training approach: starting with foundational sessions on data handling, followed by practical workshops that explore the risks of oversharing with AI.
• Reinforcing this with scenario-based examples helps employees understand the tangible impact of secure data handling.

Raising Awareness of AI-Enhanced Phishing Tactics

AI-generated phishing emails often mimic legitimate communication so well that standard red flags may be harder to spot. For instance, AI-generated phishing messages may display unnatural levels of detail or overly precise information about recent activities.

How can you strategize against it?

• Highlight recent real-world AI phishing cases and dissect them to reveal the subtle differences from genuine communication.

Practical Exercises to Recognize AI-Generated Anomalies

Learning by doing is crucial when it comes to recognizing AI-generated anomalies. Scenario-based exercises help employees get comfortable spotting suspicious patterns in emails, messages, or requests. This hands-on practice builds the reflex to question and verify unusual requests.

How can you strategize against it?

• Design interactive simulations that expose employees to realistic scenarios, such as emails that appear to be from executives or system-generated messages requesting confidential data.
• To make this training even more engaging, introduce simulated incidents where employees work in teams to investigate possible phishing attempts, providing feedback on their response strategies.

Creating an Environment That Encourages Reporting

In many cases, employees hesitate to report minor anomalies, fearing they might seem paranoid or bother their colleagues.

How can you strategize against it?

• To counter this, foster a workplace culture that values vigilance and quick action over hesitation. Share anonymized success stories within the company where early reporting helped to detect a significant security threat.

Keeping Employees Updated on Emerging AI Threats

The speed at which AI-driven threats evolve makes continuous learning essential.

How can you strategize against it?

• Hold monthly "AI Threat Briefings" where employees receive short, focused updates on the latest generative AI-based attacks and defensive strategies.
• Introduce new techniques used in deepfake scams, detailing how attackers manipulate voice or video to imitate company executives. Additionally, consider a rotating "Security Spotlight" in the company newsletter, where emerging AI threats are highlighted.

Learn how to manage AI trust, risk, and security from industry expert Avivah Litan, Gartner Distinguished VP Analyst. In this insightful presentation, Litan introduces a comprehensive framework along with essential tools and processes to mitigate AI-related risks.

Watch now: Manage AI Risks Before They Manage You l Gartner IT Symposium/Xpo

To further safeguard against generative AI risks, organizations can implement comprehensive security frameworks that ensure AI security practices are consistent and resilient across departments.

Implementing Security Frameworks

To tackle the sophisticated risks generative AI presents, organizations need robust, adaptable security frameworks. Here are key frameworks and tools to consider.

Adopting Zero Trust Architecture

Zero Trust is built on the principle of "never trust, always verify." This approach requires strict identity verification for anyone attempting to access company resources, whether inside or outside the organization. To fully implement Zero Trust, organizations can set up multi-factor authentication (MFA) and context-based access controls, which adjust permissions based on real-time factors like location or device.

Additionally, micro-segmentation divides networks into secure zones, meaning that even if a breach occurs, the threat is isolated and limited. Zero Trust not only limits unauthorized access but also reduces lateral movement within systems, preventing attackers from easily navigating through a network.

Leveraging Secure Access Service Edge (SASE)

SASE is a cloud-based framework that integrates network and security functions to protect users and data, especially in remote or hybrid environments. SASE combines components like secure web gateways, cloud access security brokers (CASB), and firewalls, all managed through a single platform.

For organizations with remote teams, SASE allows secure access to company resources without compromising performance or security. With SASE, security protocols can scale across all devices, ensuring consistent protection as your workforce grows or shifts.

Implementing Data Loss Prevention (DLP)

Data Loss Prevention (DLP) tools are essential for monitoring and protecting sensitive data from unauthorized access, transfer, or sharing. Advanced DLP systems can detect and classify sensitive data in real-time, such as personally identifiable information (PII) or intellectual property.

Integrating DLP with other tools like Zero Trust strengthens data oversight, especially in cases where generative AI could inadvertently expose or mishandle sensitive data. DLP is particularly valuable in detecting AI-assisted data exfiltration attempts and alerting security teams to unusual data movements.

Utilizing Risk-Adaptive Protection

Risk-adaptive protection involves adjusting security measures based on the context of each interaction or action within the network. This means applying stricter controls for actions considered high-risk, like downloading large volumes of data or accessing secure files from an unusual location.

By using AI to continuously assess risk levels, companies can apply dynamic policies that protect sensitive areas without disrupting normal operations. For example, an employee accessing sensitive data from an unfamiliar device might be temporarily restricted until their identity is confirmed.

Enforcing Automated Policy Compliance

Consistent policy enforcement is key to maintaining a secure environment, especially in complex AI-driven systems. Automated policy enforcement tools ensure that security policies—such as access restrictions or data handling requirements—are applied uniformly across all departments and devices.

For instance, policies can automatically restrict access to AI models for users without specific training or security clearance.

Combining Behavioral Analytics with Intrusion Detection Systems

Behavioral analytics tracks patterns in user behavior to establish normal activity levels, helping detect anomalies that may indicate a breach.

Combined with Intrusion Detection Systems (IDS), behavioral analytics can alert security teams to unexpected behaviors, such as large data downloads at odd hours or login attempts from unusual locations. Behavioral analytics can detect subtle indicators of compromised accounts, like changes in typing speed or login frequency.

Using Threat Intelligence Platforms

Threat intelligence platforms (TIPs) gather and analyze data on emerging threats from global cybersecurity networks, giving organizations real-time insights into potential risks. Integrating TIPs into your security framework allows proactive identification of new attack vectors, such as novel AI-generated malware strains or phishing methods.

Governance Framework for Generative AI

A solid governance framework is crucial for managing generative AI responsibly, ensuring both ethical practices and regulatory compliance. Here's how organizations can build a robust governance framework for generative AI:

Developing Clear Guidelines

Establishing clear guidelines on how generative AI should be used ensures that AI models align with organizational values and ethical standards. These guidelines should cover what types of data are acceptable for training AI models, the boundaries for AI use cases, and the conditions for model deployment.

Classifying and Anonymizing Data Used

Classifying data helps determine what level of protection each data type requires. Sensitive data, such as personal information or proprietary business details, should be marked for restricted use. Anonymizing data before it's used in AI training reduces the risk of accidental data leaks.

Implementing Accountability Measures

AI-generated outputs and applications in businesses can have significant consequences, making accountability essential. Establish a chain of accountability that identifies who is responsible for each model's deployment, monitoring, and decision-making outcomes.

Ensuring Compliance with Evolving AI Regulations

AI regulations are emerging and changing rapidly worldwide, with new standards on data protection, privacy, and ethical AI usage. Compliance teams should work closely with legal advisors to monitor new regulations and adapt governance policies accordingly.

Establishing Transparent Auditing and Reporting Protocols

Transparent auditing processes allow organizations to track and document how AI models are performing, what data they're using, and whether they meet established governance standards. Regular audits can reveal whether AI systems are producing unintended biases or errors.

Creating a Cross-Functional Governance Team

Generative AI governance benefits from a diverse range of expertise. A cross-functional team—consisting of data scientists, IT security professionals, legal advisors, and operational leaders—ensures that all aspects of AI use are considered.

Instituting a Responsible AI Use Policy

Organizations often rely on third-party vendors for generative AI solutions, but outsourcing doesn't eliminate risk. This policy should require vendors to disclose their own AI governance practices, including data handling and security protocols.

How can Ema help?

Designed as a futuristic Agentic AI, Ema takes on multiple specialized roles, from Customer Support to Compliance Analyst, working alongside your team to streamline workflows and automate complex tasks. Ema is built with the highest standards of security and compliance, meeting international certifications like SOC 2, ISO 27001, HIPAA, and GDPR. The proprietary EmaFusion™ model blends multiple AI models—both public and private—to maximize accuracy and avoid over-reliance on any single system.

Collaborative Cyber Defense Approaches

With the rise of generative AI threats, a collaborative approach to cybersecurity is essential. By working together, organizations can strengthen their defenses, share valuable insights, and stay ahead of emerging AI-driven threats. Here are effective ways to implement collaborative cyber defense:

Participating in Threat Intelligence Sharing Networks

Joining threat intelligence networks enables organizations to share information on emerging cyber threats, such as AI-driven phishing techniques or new forms of AI-generated malware. These networks, like the Information Sharing and Analysis Centers (ISACs), allow members to exchange real-time information on attack patterns, vulnerabilities, and successful mitigation strategies.

Collaborating with Industry-Specific Security Alliances

Industry-specific security alliances bring together companies within the same sector to address unique security challenges. For example, the Financial Services Information Sharing and Analysis Center (FS-ISAC) focuses on threats relevant to financial institutions.

Establishing Partnerships with Experts

Working closely with cybersecurity vendors and experts provides access to cutting-edge technologies and advanced threat insights. Many cybersecurity firms develop specialized tools for detecting AI-generated attacks, like deepfake detection software or advanced intrusion detection systems.

Conducting Joint Cyber Security Exercises

Joint cybersecurity exercises allow organizations to simulate and respond to AI-driven attacks in a controlled environment. For example, companies can conduct "red team/blue team" exercises with partners, where one team simulates an AI-based attack while the other team defends against it. Government agencies often participate in these exercises, providing additional resources and insights that strengthen the overall defense effort.

Engaging with Cross-Industry Knowledge

Generative AI threats affect all sectors, making cross-industry knowledge sharing valuable. Companies from different industries—such as finance, healthcare, and manufacturing—can come together to share experiences and solutions for AI-related challenges.

Wrapping Up

Generative AI has immense potential to drive innovation and efficiency, but it also brings new security challenges that organizations cannot ignore. By fostering a culture of constant improvement, collaboration, and vigilance, organizations can confidently embrace generative AI's potential while safeguarding their assets, data, and reputation.

Built with enterprise security at its core, Ema employs robust data redaction and top-tier encryption to protect sensitive information before it interacts with public AI models. This security-first approach makes Ema an ideal solution for companies that handle sensitive data.

Ready to enhance efficiency and protect your data? Hire Ema to transform your business ventures!