Cybersecurity Awareness in AI Deployment

October is Cybersecurity Awareness Month, which means our community has become laser-focused on disseminating cybersecurity news. Recently, artificial intelligence (AI) has become more prevalent in messaging. AI for good, and how AI is used to support cybersecurity. AI for bad actors, and how cyberattacks have become more sophisticated and thus more difficult to recognize by users. In the end, human behavior online plays a key role in cybersecurity.

Balance

As a leader, are you balancing AI knowledge and caution effectively?

If you become overwhelmed, will you halt innovation, security, or both?

Your strategic decisions could decrease the ROI of AI and cybersecurity investments or result in a cyber incident. ?

Creating and Aligning Strategies

Creating and aligning strategies will help.

As generative AI (GAI) tools rapidly advance, Boards, CEOs, and CISOs find themselves in the hot seat, facing complex promises and perils. First and foremost is cybersecurity and cyber resilience.

Consider three actionable steps to improve AI Cybersecurity: ?

• Training: In the age of AI, regular, impactful Cybersecurity Awareness Training is necessary. The days of requiring all members of the organization to watch videos are over. If the information lacks relatable stories to resonate with them, you cannot assume they understand the risks. Help your teams engage with the material to prepare them for facing and reporting a cyberattack.
• Diligent Behavior: Safety and Security require full cooperation from leaders and the organization. Guide your organization toward a culture focused on AI cybersecurity. Make it a company-wide team effort. There is no room for blame or shame if someone encounters a scam, fraud, or cyber incident. Encourage awareness and quick reporting and reward behavior that leads to successful outcomes. ?
• Cyber Resilience and an Incident Response Plan: Plan and rehearse a recovery. This advice is not new, but the impact of not preparing is new. Consider the ROI of AI investments if you suffer a cyberattack, reveal trade secrets, or are sued for discriminatory hiring practices. Recovery planning for potential challenges is as strategic as looking to improve the efficiency of processes and employees from your AI investments. ?

Balancing AI benefits and cyber risks is in your hands. You are in control.

Awareness vs. Mistakes

Leaders need to be aware of some common cybersecurity mistakes when deploying AI technology.

Here are three scenarios:

Shadow AI Deployments

• Scenario: Employees or departments deploy AI tools without the knowledge or oversight of the central IT or security teams, resulting in unmonitored and unsecured AI usage. For example, a marketing team might deploy an AI tool to automate customer outreach without ensuring data privacy compliance such as consent.
• Impact: Shadow AI can expose the organization to data breaches, non-compliance with regulations (e.g., GDPR), and inconsistent security practices.
• Planning: Implement a centralized AI governance framework, regularly audit and monitor the organization’s AI use, and train employees on the risks of unauthorized AI deployments.

Data Poisoning Attacks

• Scenario: Malicious actors intentionally feed corrupted or misleading data into an AI model during the training phase, skewing its output. For example, in a fraud detection AI system, attackers manipulate the data to let fraudulent transactions slip through undetected.
• Impact: Unreliable or inaccurate AI decisions could result in financial losses, legal liabilities, reputational damage, and decreased customer loyalty.
• Planning: Implement robust data validation techniques, monitor for anomalies in model performance, and ensure a secure data supply chain.

AI Model Adversarial Attacks

• Scenario: Attackers trick an AI system into making incorrect decisions through specific inputs. For example, the subtle altering of images may cause an image recognition system to misclassify objects, impacting critical sectors like healthcare or autonomous driving.
• Impact: Misclassification or incorrect predictions can lead to safety hazards, financial losses, and a loss of trust in AI systems.
• Planning: Incorporate adversarial testing during AI development and use techniques such as adversarial training to harden models against such attacks.

Proactive leadership is the name of the game in AI implementation to ensure data integrity and system security.

Planning

Let’s roll up our sleeves and look at some planning products and tools.

In this edition, we’ll focus on AI TRiSM.

AI TRiSM

The concept of AI TRiSM (AI Trust, Risk, and Security Management) focuses on building trust, mitigating risks, and securing AI models throughout their lifecycle.

Portal26 is an example of an emerging class of platforms specifically designed for AI TRiSM, addressing the need for end-to-end management, monitoring, and securing of AI assets across an organization.

Here’s the relevance of specific planning steps for the three scenarios (above):

• Shadow AI Deployments: By acting as a centralized governance platform, Portal26 prevents unauthorized AI tools from being deployed without visibility or security protocols.
• Data Poisoning Attacks: Portal26’s audit and risk management tools ensure that data integrity is maintained, and models are continuously checked for poisoning risks.
• AI Adversarial Attacks: With adversarial testing and risk monitoring, Portal26 helps strengthen AI model defenses and alerts stakeholders to potential attacks.

Portal26 stands out as an Enterprise Data Encryption Software, focusing on securing data in transit. They look at traffic data analytics becoming their clients’ eyes and ears. They inform security and business decision-makers who need analytics to make an impact – for example, Visibility, Security, Forensics, Governance, Risk, Compliance, Education, and Policy.

Although large enterprises like IBM and Microsoft are involved in AI TRiSM, smaller AI TRiSM companies compete effectively by focusing on niche markets, leveraging agility, building strong relationships with clients and partners, and most importantly, using data analytics to help customers make an impact.

Your organization may be looking for solutions that fit your specific needs. Ultimately, the tools and partners you select can make a huge difference in your security and the ROI of your cybersecurity and AI investments.

CTA

If you are interested in learning more about AI Cybersecurity or AI TRiSM, contact me on my website The Cyber Dawn, or DM me on LinkedIn.

There is hope in cybersecurity awareness and AI for good.

Thanks for your time,

Dawn

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