Defining Success for AI for Zero-Trust Strategies.

Weekly managed security newsletter from Shiv Singh, CISSP , CEO of LINEARSTACK .

Artificial Intelligence (AI) and machine learning (ML) were the talk of the IT industry in 2023/24. Many organizations focused on Zero-Trust security are interested in Generative AI. However, Zero-Trust still plays a crucial role in mitigating security risks without AI.

Zero-trust and AI expect to open up new opportunities for enhancing security to meet growing challenges for new cyber threats. However, embedding AI into zero-trust comes with its share of risks.

In this week's executive insight newsletter, Shiv Singh, CEO of Linearstack, discusses the critical role AI plays in zero-trust architectures. However, Shiv also addresses the risk and the need for extensive monitoring because of unpredictable factors regarding AI and machine learning (ML) within security solutions.

Building a Culture of Zero Trust.

AI and ML are critical components in enhancing the zero-trust security model to counter developing cyber threats, such as ransomware attacks in remote and hybrid work environments. AI plays a significant role in zero-trust strategies.

The Zero Trust security model is a powerful defense against AI-based threats. It assumes that any device or user is a potential threat, regardless of their location within the network. To protect against cyber threats, secure AI systems with strict access controls and continuous monitoring through a zero-trust model.

The Risk AI Embedded Within Security Solutions Including Zero Trust.

Deploying and relying on AI has its share of challenges.

AI security risks are potential threats and vulnerabilities from AI and and ML technologies. These risks can manifest in various ways.

AI helps organizations protect against next-generation attacks, including increases in high-velocity brute force against access control and identity management systems. Businesses should research the importance of AI security, data protection, monitoring for suspicious activity, and protecting critical infrastructure.

The following attack vectors place AI at risk. These attacks include:

Adversarial attacks:

These attacks change AI input to control the model's behavior without detection. For instance, a facial recognition system was altered to grant access to the wrong person.

Data poisoning:

Poisoned data can corrupt AI models by introducing misleading information during training, leading to unreliable outcomes.

Model theft and Inversion Attacks:

Attackers may try to steal AI models or extract sensitive information from their outputs, especially when the models are offered as a service. Even with the associated risk, embedding AI into the security controls is essential.

AI threats are strengthening because of complex AI systems and sophisticated cybercriminals. Zero Trust model must adapt to these ongoing changes.

The Role of an MSSP and AI-Powered Zero Trust.

The Zero Trust model is crucial to addressing threats to growing AI technologies. It assumes no inherent trust and verifies all activity across the IT environment. This model includes granular access controls, thorough visibility, and ongoing security monitoring, making it a valuable tool in combating AI-related threats.

Organizations mandated by compliance and regulations to ensure their security adaptive controls stay monitored and enable an effective incident response process should leverage a partnership with a managed security service provider (MSSP).

MSSPs like Linearstack understand the complex world of AI and security solutions, especially regarding zero trust. Clients struggling with retaining senior security and IT operations engineers will find Linearstack valuable to their organization.

Call-To-Action

Are you planning to roll out zero trust powered by AI? Do you have the proper in-house support model?

If not, Linearstack is your MSSP to help with your zero trust and AI strategy and deployment.

Schedule an initial consultation with our security and zero-trust experts today!

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