Artificial Intelligence (AI) has not just been promised but has also been delivered. An AI workplace has many advantages over a workplace where manual processes and systems are still prioritized.
When we say ‘AI led human assisted workplace,’ we mean an environment where AI takes the central stage to analyze, predict, and summarize data patterns, automate workflows to improve efficiency, identify and differentiate between alerts and threats, and suggest actions. In short, AI becomes the single source of truth for these workplaces by building personalized, responsive interactions.
But if AI takes care of everything, who takes care of AI?
Believe it or not, only 24% of current AI projects are being secured. In a survey conducted by IBM, 70% of respondents said that innovation takes precedence over security when it comes to deploying AI.
If you are a Chief Information Officer (CIO) at an AI led workplace, the biggest challenge ahead of you right now is to deploy AI that seizes opportunities without compromising your organization’s security.
This article brings you the biggest threats ahead of CIOs regarding AI deployment and solutions to address them.
A Glimpse of AI Regulations Around the World
Before diving deeper into the critical security concerns associated with AI tool deployment, below are a few global AI regulations that CIOs must be aware of:
AI Regulations in the US
AI Training Act: The AI Training Act US 2022 aims to educate employees working in the procurement, logistics, project management, etc. industries about the use cases and risks of AI. This Bill ensures that the Office of Management and Budget (OMB) either creates or provides an AI training program to assist organizations in the process of informed acquisition.
AI Regulations in Europe
EU AI Act: The EU AI Act, passed on 13 March 2024 by the EU parliament, applies to developers and deployers of AI-powered digital health tools. Though a significant part of this law is still unclear, it primarily focuses on building a controlled framework for prospective AI system providers to develop, train, validate, and test AI models based on appropriate real-life situations.
AI Regulations in India
Digital India Act: The Digital India Act 2023 attempts to protect Indians on the internet through accountability, developing a dedicated adjudicatory mechanism for civil and criminal offenses without compromising on the prospects of digital technology.
Other critical data protection regulations include:
General Data Protection Regulation (GDPR): GDPR ensures that an organization's data protection practices align with the regulation's requirements. This includes implementing appropriate technical and organizational measures to ensure data security, such as encryption, access controls, and regular risk assessments.
California Consumer Privacy Act (CCPA): CCPA (California Consumer Privacy Act) requires the implementation of measures to protect the personal information of California residents. This includes providing clear privacy notices, honoring consumer rights such as access deletion, opting out of data sales, and maintaining reasonable security practices.
Key Security Concerns for CIOs for Deploying AI Technology
When AI deployment goes wrong, it could lead to numerous security risks, including data breaches, permanent damage to your brand reputation, and endless penalties associated with regulatory risks.
While there are plenty of security challenges associated with AI technology implementation, below are the three most significant security concerns that CIOs must know:
Data security: protecting training data and personally identifiable information (PII)
The popularity of AI and its exceptional outputs have intrigued employees worldwide, who are exploring how it can make their work lives easier. Be it OpenAI’s ChatGPT, Google’s Gemini, or Anthropic’s Claude, more and more employees are using these platforms regularly to reduce the load of their manual tasks.
By using these tools, employees are handing over sensitive data to these platforms to train their models. But how secure are these tools? Moreover, these are only a few names. What if employees use less-established AI tools without security evidence to automate their tasks?
A Gartner study found that 41% of employees had created, acquired, or modified technology without the IT team’s knowledge as of 2022. This number is expected to climb to 75% by 2027.
Alternatively, training the Large Language Models (LLMs) of a GenAI tool is expensive. You need strong infrastructure support and technology resources to execute it. This is why many GenAI tools today are built with pre-trained foundation models. A pre-trained model is trained on large, generic-purpose datasets capturing a vast knowledge range. You can later finetune it with specific data and use cases.
Using pre-trained or fine-tuned models may sound intriguing due to their budget advantages. However, when opting for such LLMs, CIOs need to handle the risks of both training data and the concept of model development.
Best practices
- Implement multi-factor authentication (MFA) and encrypted storage for training data
- Regularly audit access logs to detect and respond to unauthorized access attempts
- Establish communication policies that address the use organizational datasets (confidential or PII) within third-party tools or public models
- Employ data anonymization and PII redaction capabilities to minimize the risk of accidental exposure. Regularly update your data protection policies to comply with evolving regulations like GDPR and CCPA
Access controls: LLM models and AI system access
Many large organizations are building and training LLM models from scratch. In such cases, the database is purely these organizations’ distinct datasets. Therefore, the biggest threat in these scenarios is associated with these organizations’ own datasets. The organization is responsible for protecting its source data with strict access control frameworks so it doesn’t get compromised.
Any GenAI solution’s ultimate assessment is how accurate and unbiased its outputs are. If an organization does not protect its training database or practice ethical AI strategies, the trustworthiness of its GenAI solutions will be at risk.
Best practices
- Use role-based access control (RBAC) and implement strict approval processes for model modifications. Regularly review and update access permissions to reflect changes in personnel and roles
- Avoid granting excessive privileges. Conduct regular access reviews and implement least privilege principles to reduce the attack surface
Monitoring and governance: AI traffic monitoring, governance and data framework
Developing and implementing an AI governance framework across the organization is a critical challenge. It is an extensive, expensive, and long process that requires the involvement of skilled employees. Any deviations lead to inefficiencies in the model and biased and flawed GenAI outputs. All these could raise serious questions about a framework’s effectiveness, not to mention regulatory dilemmas and reputational damage.
Best practices
- Deploy AI-specific security monitoring solutions and integrate them with your broader security information and event management (SIEM) system. Use automated alerts to respond quickly to suspicious activities
- Form a cross-functional AI governance committee, including IT, legal, compliance, and business unit representatives. Maintain an inventory of AI deployments and perform regular compliance checks
- Develop and enforce data standards and policies. Use data lineage tools to track the origin and transformations of data used in AI models
More Tips for CIOs Handling GenAI Tools
Engage with security certifications when selecting an AI tool: Look for AI solutions with security certifications like ISO/IEC 27001 or SOC 2. These certifications indicate a commitment to stringent security standards. Develop mandates around employees disclosing the type of GenAI tools they are using and create dedicated guidelines related to the type of data they can or cannot use while using such tools
Conduct regular vendor risk management: Conduct thorough risk assessments of AI vendors. Ensure they have robust security measures in place and can demonstrate compliance with relevant regulations. Encourage employees to test different tools before making a decision, and ask them to share their concerns with CIOs and other stakeholders while evaluating a new vendor. Develop a culture of innovation but not at the cost of compromising security
Conduct employee training: Regularly train employees on AI security best practices and emerging threats. Foster a culture of security awareness across the organization. Set realistic expectations for employees. They should be able to make decisions about when to use AI and when to trust their capabilities.
According to Jeff Stovall, DEI Committee lead for SIM, “There is a general perception that AI and genAI can solve all manners of problems. It’s not a general-purpose tool to accomplish everything. AI doesn’t do everything well, and you cannot at this stage utilize AI to completely replace the human element; it’s a human augmentation tool.”
Incident Response Planning: Develop and regularly update an AI-specific incident response plan. Ensure it includes procedures for handling data breaches, model theft, and other AI-related security incidents
Regulatory Compliance: Stay informed about global AI regulations. Ensure your AI deployments comply with laws such as the EU's AI Act and the California Consumer Privacy Act (CCPA), GDPR, and other location-specific AI regulations. Internalize the contextual AI acts and keep your entire team aware of these terms
Conclusion
With the AI wave around us, it is not ideal to stay away from it. And you shouldn’t. However, as a CIO, you must focus on selecting responsible Gen AI tools that train their LLM models ethically and won’t be a ticking bomb for your business, employees, and customers.
If you are looking for a responsible, ethically trained AI customer experience platform, we suggest Alltius.
Talk to the team about how Alltius can automate your support operations without compromising privacy.
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