Security for AI vs. AI for security

AI for security uses artificial intelligence to strengthen cyber defense. It involves embedding machine learning, deep learning and generative AI into security technologies to increase their efficiency and effectiveness. 

For example, a machine learning powered risk rating in a vulnerability management solution helps prioritize business-impacting vulnerabilities so your practitioners would only need to manage a smaller number of exposures instead of mitigating all critical and high-severity cases, an ongoing struggle for many organizations. 

Generative AI has also contributed to the rise of security assistants, which have proven to boost IT security team productivity overall and help uplevel more junior team members. 

It makes security operations faster, more adaptive and more capable of handling data at a scale beyond human capacity.

Security for AI protects against exposures to enterprise AI and shadow AI. As you build your own AI models or adopt third-party tools like ChatGPT and Gemini, they expand your attack surface. It protects AI models, training data and your underlying infrastructure from a new class of threats, like adversarial attacks, data poisoning and model theft.

Securing AI tools is a growing priority as teams across your organization adopts: 

Productivity tools like:

• ChatGPT
• Gemini
• Microsoft Copilot

Open source AI models like:

• Llama
• Mistral
• Bloom

AI libraries like:

• PyTorch
• TensorFlow
• Scikit-learn

Data storage like:

• S3 bucket (AWS)
• Azure Blob Storage
• Google Cloud Storage

Large language models (LLMs) like:

• GPT-4
• Claude 3
• PaLM 2

But the more these tools become part of your organization’s security, software development, finance, marketing and other workflows, the more they expose new risks.

Here, security for AI refers to the people, processes, policies and tools you need to protect AI infrastructure, use patterns and outputs. 

In many environments, this starts with developing a policy for secure, ethical and appropriate use of AI, along with identifying shadow AI, which are AI tools your organization hasn’t officially sanctioned or secured.

Ask:

• Do our developers paste source code into public AI tools?
• Do business users upload sensitive data to ChatGPT?

Security for AI begins with inventory.

Ask:

• Which models do we use?
• Where have we deployed them?
• Who can access them?

That includes:

• Sanctioned AI
• Unsanctioned AI use
• Data residency
• Data retention
• Model training policies
• API use between LLMs
• AI in cloud systems
• Fine-tuned model governance

Security for AI is important, whether you’re building with open-source models, using third-party AI tools or embedding AI into internal applications. 

Once you know where the models live, you can apply controls like:

• Role-based access
• Data classification and protection
• Monitoring of inference behavior
• Policy enforcement to stop unauthorized access or data leaks

For cloud-hosted AI infrastructure, your controls should secure:

• Model weights and training data
• Infrastructure as code (IaC) deployments
• APIs that expose model inference
• Associated services like vector databases

Secure AI use and prevent data leaks.

One of the biggest risks with AI is how people use it. 

Employees who paste sensitive information into AI tools without guardrails may unknowingly expose proprietary data. 
 

What could go wrong

• Developers might drop source code into public AI tools.
• Marketing teams may upload customer lists.
• Business users could share confidential documents with a chatbot. 

If someone inputs any of this data into someone else’s model training, even well-meaning actions can result in privacy violations or intellectual property leaks.
 

How to reduce your risk

• Set clear rules.
  • Create and enforce acceptable use policies that define where, when and which data your employees can use in public AI tools.
• Discover AI use across your organization.
  • Start by identifying who’s using what via AI monitoring tools. That includes browser plugins, apps and cloud-based AI services, approved or not.
• Stop sensitive data from leaving.
  • Use DLP policies tuned for AI interactions to flag source code, personally identifiable information (PII) or internal documentation before enabling external sharing. You can also use firewalls to block access to unsanctioned AI IP addresses; just note that employees will find a workaround, and you may always play catch-up with new services. 
     

Lock down your AI development environment.

AI development uses a complex stack like cloud services, APIs, training data, vector databases and machine-learning operations (MLOps) platforms. Each layer introduces potential exposure. 

Here, AI-SPM is key.
 

What could go wrong

• Poor configurations can expose model endpoints, training data or permissions.
  • An attacker who finds an open API or weak IAM role can steal model weights, access sensitive data or even manipulate AI behavior in production.
     

How to secure your AI models and libraries against vulnerabilities

• Inventory your AI ecosystem.
  • Track everything, not just the models, but also services like SageMaker, Bedrock, Azure AI, Vertex AI and supporting infrastructure.
• Scan for misconfigurations.
  • Catch public buckets, over-permissioned roles or API exposures before they create risk.
• Control access.
  • Apply strict RBAC and enforce least privilege so only approved identities can access your AI resources.
• Secure your supply chain.
  • Use tools like an AI bill of materials (AIBOM) to monitor third-party dependencies and pre-trained model risks.

Protect AI models at runtime.

AI models respond to user input, and attackers know how to exploit that. Runtime defenses help you spot and stop adversarial attacks before they cause real damage.
 

What could go wrong

• Data poisoning, where attackers taint training data to introduce hidden vulnerabilities.
• Evasion attacks, where threat actors create inputs to fool your model into misclassifying or misbehaving.
• Model extraction, where query patterns can reverse-engineer logic or leak sensitive training data.
• Prompt injection, where bad actors use malicious prompts to manipulate LLMs into generating harmful output or revealing hidden instructions.
   

How to defend it

• Train for adversarial resilience. Use adversarial samples during model training to build stronger defenses.
• Filter and validate inputs. Sanitize queries before they reach your model to block injection attacks.
• Monitor model behavior. Watch for output anomalies, refusal spikes or patterns that suggest misuse.

A complete security for AI strategy covers all three layers. You need visibility into how users interact with AI, guardrails for infrastructure and entitlements, and defenses for models in production. These protections enable your teams to innovate without opening the door to avoidable risk.

How does AI benefit cybersecurity technologies?

AI for security augments security tools in your stack, so you can more quickly and accurately identify, prioritize and respond to exposures and threats. 

AI analyzes massive volumes of telemetry in real time to find patterns, map risk and suggest the best actions to close exposures and remediate threats.

Predictive threat analysis

AI models look at past attacks and threat intelligence to predict what’s coming next, so you can harden systems before a threat actor exploits them.

For example, AI can pinpoint which vulnerabilities attackers are most likely to target based on threat actor behavior, exploit availability and asset exposure. 

Solutions with vulnerability prioritization tools use machine learning to surface the real risks from thousands of findings, cutting through the noise of CVSS scores and pointing you toward the flaws that matter.

 

How AI supports proactive security operations

AI platforms combine context from multiple sources about behavioral anomalies, threats, vulnerabilities and other exposures, including cloud configs and identity permissions, to tell you what’s at risk and why it matters.

Here are some ways AI can improve your cyber defenses:

See how Tenable risk scoring helps streamline vulnerability prioritization with more accuracy. 

Threat and anomaly detection

AI builds a dynamic baseline of what’s typical in your environment. That includes logins, service behaviors, API activity and cloud workload operations.

An AI tool in your cybersecurity solution can automatically catch unusual activities like login attempts from suspicious locations or containers poking around where they shouldn't.

Because this kind of anomaly detection doesn’t rely on predefined signatures, it’s especially effective at spotting novel threats, zero days and insider threats that traditional signature- and rules-based tools are likely to miss.

Intelligent attack path mapping

AI helps you see the big picture. By processing exposure data from across your attack surface in real time, it lets you see which vulnerabilities, misconfigurations and excessive permissions across your cloud and on-prem environments combine to create high-risk attack paths leading to your crown jewels.

You can use that insight to proactively break attack paths, whether by revoking a permission, remediating a misconfiguration or isolating a risky asset.

Analyst augmentation with generative AI

Generative AI makes complex data easier to understand. It can summarize exposure paths, explain what a vulnerability does and outline how to fix it, all in plain language.

Instead of digging through dashboards or knowledge bases, your SOC and exposure analysts can ask natural-language questions and get immediate, context-rich answers. It boosts efficiency and gives analysts time to focus on higher-value work.

The catch? If you don't apply the same level of scrutiny and access control to your generative AI tools, you risk introducing new exposures, putting sensitive data, proprietary models and user trust at risk.

AI for security doesn’t remove human oversight. It offloads manual, repetitive tasks that consume your team’s time and lead to burnout.

You can automate alert triage, correlation and data summarization so your SOC analysts can focus on better understanding attacker intent, investigating incidents and building more mature cyber defenses. 

Combining AI’s speed and scale with human expertise gives you a powerful advantage. 
 

See how Tenable AI capabilities can help you manage exposures that novel AI-driven attacks introduce and identify unauthorized AI usage across your environment.

People use the term AI-powered security loosely, but when you dig into the AI security platforms that perform best in practice, they all share one thing: specialization.

• Some models are great at prediction.
• Others look for pattern recognition or natural language interpretation. 

The more effectively a solution maps each model to a security use case, the stronger and more efficient your outcomes.

Here are some of the major types of AI models your teams can use to enhance security and what they do best:

Supervised machine learning for pattern-based prediction

• What it does: Learns from labeled historical data to predict outcomes or classify new inputs.
• Where it fits in security: You need to know which vulnerabilities pose real risk. Supervised machine-learning models can learn from trillions of data points, past exploit trends, attacker behavior and asset criticality to forecast which new vulnerabilities attackers are most likely to exploit.
• What it looks like in action: Platforms that use this model can assign a predictive risk score to each vulnerability based on actual threat activity. It’s a data-informed alternative to static CVSS scores and helps you reduce alert fatigue.

Deep learning to detect complex threats

• What it does: Neural networks, like long short-term memory networks (LSTMs) and convolutional neural networks (CNNs), analyze network traffic patterns, user behavior and file access logs to uncover connections that would take human analysts hours to find.
• Where it fits in security: Deep learning is essential for dealing with sophisticated threats that don't follow typical attack playbooks. These models catch things that traditional rule-based systems miss entirely, especially when attackers try to blend in or use new or unknown techniques.
• What it looks like in action: Think about malware designed to evade traditional detection by altering its appearance. Deep learning spots the underlying behavioral patterns, even when attackers intentionally modify the code to look new. Or consider a scenario where someone with legitimate access starts doing things that aren't technically against policy but feel wrong, like accessing files in an unusual order or at odd times. The system picks up on these subtle behavioral shifts.

Knowledge graphs to map how threats spread

• What it does: Connects entities, users, assets, permissions and vulnerabilities into a visual, searchable relationship web.
• Where it fits in security: These models power attack path analysis. Instead of treating risk as isolated findings, they show how attackers can string together multiple exposures to reach high-value assets.
• What it looks like in action: A knowledge graph might surface a toxic combination like a public-facing server with a known flaw that connects (via over-permissioned service accounts) to your production database. It tells you where to intervene to block an attack path.

Generative AI and NLP for analyst enablement

• What it does: Parses and generates natural language so complex security data is clear, searchable and actionable.
• Where it fits in security: Instead of digging through dashboards, your team can ask plain-language questions and get human-readable summaries of exposures, threats and response steps.
• What it looks like in action: An analyst can ask, “How do I fix this exposure?” or “Which vulnerabilities affect our internet-facing assets with admin access?” and get contextual, accurate answers immediately. It reduces investigation time and makes security workflows more accessible to non-specialists.

Why model choice matters

When a vendor claims to use “AI,” you should ask: 

• What kind?
• For which problems?
• How much data did the vendor use to train the AI model?
• How frequently does the vendor update the model?
• How does the solution’s risk-scoring model compare against CVSS, CISA KEV or EPSS?

There’s a big difference between a platform that applies one generic algorithm and one that strategically combines multiple models, each tuned to a specific task. 

The most advanced AI for security platforms integrate all of the above, using machine learning for vulnerability prediction, knowledge graphs for attack path mapping and generative AI for response guidance.

These distinctions will shape how fast your team can move, how accurately they can respond and how effectively you reduce real risk.

In the U.S., the primary standard guiding AI governance is the NIST AI Risk Management Framework (AI RMF). It’s a voluntary framework designed to help you manage AI risks and support the development of trustworthy and responsible AI systems.

The framework has four core functions: govern, map, measure and manage. 

The NIST AI RMF offers a blueprint for responsible AI governance and risk management, so your teams can build AI systems you can trust.

Putting that framework into action takes more than good intentions. It requires practical tools that give you deep visibility and usable insights. 

Platforms with strong AI discovery and security features — for example, ones that flag both approved and unapproved AI use, models, and infrastructure — play a direct role in the framework’s map and measure steps by building a full inventory and surfacing related risks. 

By continuously monitoring AI exposures and spotting vulnerabilities, these tools also help you manage risks through focused remediation. 

In the end, modern security solutions can help you bring responsible AI governance to life and adopt AI more safely.

Remember, though, that frameworks are not a linear checklist. They’re a guide for continuous processes to manage risk throughout your AI lifecycle. 
 

Govern 

The govern function is the cornerstone of the AI RMF. It establishes and cultivates a risk management culture. It creates policies, defines accountability and ensures you have the right processes to support the other three functions. 

While people and policy drive governance, technology underpins and enforces it. Effective governance is impossible without the comprehensive visibility and data that security tools provide.

Key govern activities

• Create guidelines for AI deployment and set your risk tolerance upfront
• Define and assign roles and responsibilities for AI governance
• Foster a culture that prioritizes open communication about AI risks
• Create processes to manage risks from third-party AI components
   

Map, measure and manage

Once you have a strong AI governance foundation, you should engage in a continuous cycle of mapping, measuring and managing AI risks.

Map

The map focuses on context and discovery. Before securing an AI system, you must understand its purpose, components and potential impact.

Mapping involves:

• Building a comprehensive AI system inventory, including models and data sources, AWS, Azure and GCP AI services, unsanctioned shadow AI software and browser plugin detection.
• Documenting system context, including intended goals and capabilities.
• Identifying potential risks for all components, including third-party elements.

Measure

In the measure phase, you evaluate the risks from mapping to see how much you can trust the infrastructure running your AI systems.

Measuring involves:

• Analyzing data pipelines and cloud infrastructure for potential security exposures, such as publicly accessible data buckets, insecure configurations in AI services like Amazon SageMaker, or identity and access management (IAM) roles that give people way more access than they need
• Continuously testing and evaluating AI assets for vulnerabilities and misconfigurations
• Establishing and tracking metrics related to AI security and compliance over time

Manage

The manage stage treats the risks you have mapped and measured. You should allocate resources to address the most significant risks according to your defined risk tolerance.

Managing involves:

• Applying security controls to reduce risk, like mitigation technical actions such as fixing cloud misconfigurations, revoking excessive permissions and encrypting data.
• Tip: Use a security platform to guide actions with step-by-step remediation instructions.
• Shifting risk to another party, for example, through cyber insurance.
• Deciding not to deploy an AI system if its risks are unacceptably high.
• Formally accepting a risk that falls within your organization’s defined tolerance level.

A framework like the NIST AI Risk Management Framework (AI RMF) provides a crucial blueprint for responsible AI, but it it’s a theoretical exercise without the right approach to put it into action.

This is where AI Aware comes in. It provides data for AI governance to help build and enforce policies.

AI Aware is about having the visibility, understanding and control you need to effectively manage AI risks within your organization. It's about moving beyond conceptual guidelines to practical implementation.

A platform like Tenable Cloud Security offers the technical foundation you need to cultivate an AI Aware posture. It provides a comprehensive inventory to map your AI landscape, continuous analysis to measure your security posture, actionable guidance to mitigate your risks and the organization-wide visibility required for effective AI governance.

Ready to move beyond theoretical frameworks and truly understand and manage your AI risks? Discover how Tenable Cloud Security can help.

As a CISO, exposure management vendors probably bombard you with bold claims about their AI-powered solutions. But many just automate tasks. 

Your investment in AI security should do much more than that. It should also reduce business risk and strengthen your security program. 

Here are some key questions every CISO should ask to gain insights into how to evaluate an AI security solution.
 

Does the solution clearly connect its AI to real security outcomes, or does it just list technical features?

Consider:

• Does the vendor explain why it uses specific AI techniques, beyond just listing models or buzzwords?
• Can the vendor tie its AI architecture to outcomes like faster remediation, improved MTTD/MTTR, or reduced exposure?
• Is the AI optimized for your use case (e.g., vulnerability prioritization, entitlement risk), or is it a bolt-on?
• Does the vendor provide proof or metrics demonstrating the AI’s real-world security impact?

A vendor that leads with outcomes, not algorithms, is more likely to deliver strategic value.
 

Can the AI communicate clearly with humans, not just generate data?

Consider:

• Does the platform explain findings in a common language analysts can act on without decoding?
• Can it translate technical alerts into context for business leaders, risk teams or auditors?
• Does it support faster triage by showing what’s at stake and why it matters?
• Is the output usable across teams, not just IT or security?

AI that improves communication builds trust and accelerates action. If the platform can’t speak your team’s language, it won’t support collaboration or response.
 

Does the AI adapt to your business needs or force you to adapt to it?

Consider:

• Can the AI learn from your environment, including how your users behave, and which assets matter most?
• Does it adjust risk scoring based on your specific industry and environment, not a generic benchmark?
• Is the output tailored to your actual attack surface, or does it treat every organization the same?
• Can it evolve as your environment changes, or is it locked into static assumptions?

AI that can’t adapt to your business won’t help you manage real risk. It will miss what matters or distract you with what doesn’t.
 

Does the AI support risk-based decision-making rooted in exposure and impact?

Consider:

• Does the AI prioritize findings based on actual risk, not just severity scores?
• Can it distinguish between theoretical vulnerabilities and those with real exposure paths?
• Does it account for exploitability, asset criticality and adversary behavior, not just static CVSS scores?
• Is it helping your team focus on what reduces risk fastest, or spreading them thin across low-priority noise?

AI that enables risk-based security empowers you to act with intent so you can focus your limited resources on exposures that matter most.

Tenable AI tools support both sides of the AI-security equation: using AI to strengthen cybersecurity and securing the AI systems your business builds or adopts. 

This dual focus helps reduce risk across two critical fronts: protecting your infrastructure and safeguarding your AI footprint.

You’ll see this in three core offerings:

• ExposureAI powers threat detection, exposure analysis and prioritized remediation.
• AI-Aware vulnerability management helps make smarter patching decisions based on actual risk context.
• AI-SPM secures your AI models, infrastructure and entitlements.
   

Use AI to strengthen cyber defense with ExposureAI

ExposureAI is the generative engine behind the Tenable One Exposure Management Platform. It processes more than a trillion data points to help you detect, understand and decrease risk with precision.

ExposureAI maps the relationships between your assets, users, cloud services, identities and vulnerabilities. Built on Tenable’s Exposure Data Fabric, it pulls in, normalizes and connects scattered security data from across your entire attack surface. That data fabric turns disconnected findings into a rich, deeply linked web of insight.

And that connected view matters. 

Instead of just flagging isolated issues, ExposureAI can detect complex, multi-step attack chains and add precision to every alert. Think of it like a knowledge graph. By structuring your data in a relational way, it makes it possible to trace attack paths from the initial entry point all the way to your crown jewels.

With that full picture, your team sees not just where risk exists, but how everything ties together to create real exposure. It speeds up how you prioritize and fix issues. 

This foundation gives ExposureAI’s models the deep context they need to deliver spot-on insights and clear next steps, changing the way you find, understand and shut down risk across your environment.

See how ExposureAI can help you cut through all the vulnerability noise to focus on actual risk to your business. 
 

Prioritizing vulnerabilities with AI-Aware

AI-Aware improves traditional vulnerability management workflows by using machine learning to focus your team on the weaknesses that represent your biggest threats right now.

Rather than relying on static CVSS ratings, the system considers exploitability, exposure paths, threat intelligence and business context to prioritize vulnerabilities based on real-world risk.

AI-Aware reduces noise and accelerates patching by highlighting which flaws attackers are most likely to exploit in your environment. It helps you shift from reactive vulnerability management to a risk-based strategy.

See how AI-Aware enhances risk-based prioritization.
 

Securing your AI stack with AI-SPM

As AI adoption scales, so does your attack surface. AI-SPM helps you discover, harden and govern the cloud services, models and entitlements that power your AI initiatives.

AI-SPM detects AI-related infrastructure across AWS, Azure and Google Cloud. It pinpoints how users interact with platforms and flags unauthorized browser extensions, packages or shadow AI services that business users access.

It integrates with cloud infrastructure and entitlement management (CIEM) tools to enforce least privilege. It tracks who accesses your models, APIs and sensitive data so you can detect misuse early and remain compliant.

The Tenable vulnerability priority rating (VPR) uses machine learning to assign dynamic risk scores based on multiple real-world factors, not just a static CVSS score, to help your team prioritize actual risk, instead of drowning in alerts.

VPR incorporates:

• Exploit availability and weaponization
• Active threat intelligence from public and dark web sources
• Asset exposure and network context
• Temporal trends and attacker behavior

Example:

• CVSS may rate two vulnerabilities 9.8, but threat actors only actively exploit one in the wild.
• Instead of rushing to fix both based on that score, VPR applies other risk scoring metrics to assign higher risk to the one with actual threat activity. VPR gives you the insight you need to know which to patch first.

Want to see how ExposureAI and AI-SPM work together to secure your environment and your AI initiatives? Explore Tenable AI-powered cybersecurity solutions.