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AI security and adoption for your organization

Increase your organization’s AI security with nexos.ai

Reduce the risk of data leaks
Give secure access to a pre-approved set of LLMs with your policies in place
Gain full LLM observability and more

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How can nexos.ai help you with your AI security?

nexos.ai is your company’s secure, web-based environment for using AI safely, visibly, and at scale.

Simple and secure LLM integration

Access multiple LLMs securely on one AI platform - no setup hassle. Set LLM cyber security policies and stop data leaks from day one.

Full AI observability

See who and how is using AI, and what data is being processed. Monitor performance and track queries with detailed logs and traces.

AI guardrails

Prevent risky prompts and responses by setting custom input and output rules.

Team management

Control who can access which models and how much they can spend with role-based permissions.

The importance of AI security

You need clear rules on what data can be accessed, shared, or returned by if you want your employees to interact with AI and LMM agents safely.

Prevent data leaks

Define what users can upload and what information should never appear in AI-generated results. Input and output guardrails ensure that sensitive company or employee data never appears in AI-generated results.

Prevent data leaks

Easy integration and oversight

Decide which LLMs can be used via single, secure AI gateway. Route all AI activity through one protected access point, reducing the complexity of managing multiple vendors and APIs.

Access control

Set organization-wide rules for safe and responsible AI usage across teams, backed by granular user permission and management control. Track, modify, or revoke permissions in real-time, ensuring productive AI use without compromising on safety.

Full visibility and control

Know exactly how AI is used across your business. Monitor queries, track usage patterns, and audit interactions across teams from a single, centralized dashboard. Make internal reviews and security assessments from a single, centralized admin dashboard.

Full visibility and control

Know exactly how AI is used across your business with AI governance. Monitor queries, track usage patterns, and audit interactions across teams from a single, centralized dashboard. Make internal reviews and security assessments from a single, centralized admin dashboard.

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Understanding AI security risks for enterprises

Leveraging AI technologies comes with its own set of risks. No LLM or AI agent is without security flaws, among other challenges, and some of them can be difficult to overcome. The first step is to understand the AI security threats for your business so you can overcome them effectively by choosing the right AI security solutions.

AI cybersecurity threats

AI cybersecurity threats can be direct, such as AI-powered attacks, or indirect, like data leakage from poorly secured AI models.

Possible consequences

Financial loss, damaged reputation, regulatory penalties.

Main causes

Poor AI model governance, data privacy weaknesses, model vulnerabilities, lack of monitoring.

AI data access and potential data leaks

AI data access and potential data leaks refer to the unauthorized exposure or misuse of sensitive information when interacting with AI systems.

Possible consequences

Confidential information exposure, regulatory violations, financial and reputation damage.

Main causes

Poor user behavior, insufficient data sanitization, overly permissive AI systems, lack of visibility.

AI exploitation

AI exploitation occurs when attackers or unauthorized users manipulate AI systems to behave in malicious or unsafe ways.

Possible consequences

Data breaches, deepfakes, bypassing AI content restrictions.

Main causes

Prompt injection, jailbraking, weak input validation, over-permissive model access, insufficient monitoring and logging, poor isolation in integrations.

Model supply chain risk

Model supply chain risk evolves when organizations increasingly rely on third-party AI models, datasets, and APIs because these external components may come with unknown vulnerabilities or hidden backdoors.

Possible consequences

Data leaks, malicious AI model behavior, AI component exploits.

Main causes

Lack of vetting or validation of third-party models, dependency on closed-source LLMs, no supply chain visibility.

Shadow AI and unapproved use

Shadow AI is a practice when employees independently use AI technologies without security team’s oversight.

Possible consequences

Lack of audit trail, access control, or accountability for AI inputs and outputs, regulatory non-compliance, increased attack surface.

Main causes

Lack of internal policy and training, using AI for productivity shortcuts, no approved AI alternatives.

Best practices for interaction with AI systems and AI models

To navigate the balance between AI’s benefits and security risks, organizations should develop clear, purpose-built LLM security strategies. These frameworks aid in designing and deploying AI systems. Implementing AI security tools, such as nexos.ai., should also be taken into consideration. While each institution’s approach is unique, several widely adopted best practices to streamline security operations are worth mentioning, like GDPR AI compliance.

Establishing strong data governance

Data governance and risk management processes are essential for protecting sensitive information throughout the AI lifecycle without compromising model performance. By using high-quality, relevant training data and continuously updating AI models with fresh inputs, organizations can improve accuracy while letting their systems stay alongside evolving threats.

Seamless integration with security infrastructure

Integrating AI into existing cybersecurity tools can strengthen organization's defenses without introducing the downtime or complexity often associated with deploying entirely new solutions or

decentralized AI security.

Emphasizing transparency and ethics

Transparency in AI systems helps build trust and accountability. Open communication about how AI is used enables early detection and mitigation of bias, ensuring more fair and responsible outcomes.

Securing the AI itself

AI can strengthen cybersecurity, but it also needs protection of its own. Adopting AI security software, such as nexos.ai, helps safeguard both the AI systems and the sensitive data they process.

Continuous monitoring and evaluation

Ongoing oversight of AI systems with the help of logs and traces is essential for meeting regulatory standards, improving effectiveness, and staying under the budget.

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AI security and adoption for your organization

How can nexos.ai help you with your AI security?

Simple and secure LLM integration

Full AI observability

AI guardrails

Team management

The importance of AI security

Prevent data leaks

Easy integration and oversight

Access control

Full visibility and control

Take the first step toward AI security

Understanding AI security risks for enterprises

AI cybersecurity threats

AI data access and potential data leaks

AI exploitation

Model supply chain risk

Shadow AI and unapproved use

Best practices for interaction with AI systems and AI models

Establishing strong data governance

Seamless integration with security infrastructure

Emphasizing transparency and ethics

Securing the AI itself

Continuous monitoring and evaluation

FAQ

Don’t AI agents already have security measures?

Is implementing AI security infrastructure difficult or complicated?

What are the main challenges of securing AI systems?

Can AI security be automated?