AI System Penetration Testing: The Next Frontier

Key Takeaways

• AI system penetration testing is now urgent. 97% of AI-related breaches hit firms with no proper access controls. The AI cybersecurity market should grow from $25 billion to over $93 billion by 2030.
• Old pen testing methods miss AI-specific flaws. Those flaws include prompt injection, model extraction, data poisoning, and adversarial attacks. You need frameworks built for AI, such as the OWASP Top 10 for LLMs and MITRE ATLAS.
• Some firms pair AI penetration testing with AI-powered security tools. They save $1.9 million per breach on average. They also cut response times by up to 80 days.

The AI Security Imperative

We have entered a new era of cybersecurity. AI now runs through daily business. It has changed how you work, how you compete, and how you serve buyers. It has also changed the threats you face. Your old security tools were never built for this.

Look at the gap. 98% of firms now use AI in some form. Only 66% test their AI systems for flaws on a regular basis.

That is one of the biggest blind spots in business today. IBM's 2025 Cost of a Data Breach Report makes the point. Of the firms hit by an AI-related breach, 97% lacked proper AI access controls.

The stakes are high. The global AI in cybersecurity market was worth about $25 billion in 2024. It should top $93 billion by 2030. That is a compound annual growth rate of 24.4%.

The jump shows how fast firms now lean on AI. It also shows how much they must guard it.

Breaches cost real money. They average $4.44 million around the world. In the United States they climb to $10.22 million. Add downtime, brand harm, and fines on top of that. Skip AI-specific testing and you carry all of that risk.

This is where AI system penetration testing comes in. It is the next frontier in cybersecurity. It is not the same as network penetration testing or an application security assessment. AI pen testing needs new methods, new tools, and new skills.

The flaws it hunts do not exist in normal systems. Prompt injection can steer a large language model (LLM). Data poisoning can rot a machine learning model. Threats like these need a test plan built for AI.

This guide covers what you need to know. You will learn what AI penetration testing is. You will learn why it matters. You will see how it differs from old-style security testing. You will also learn how to build a program that keeps pace.

Understanding AI System Penetration Testing

What Is AI System Penetration Testing?

AI system penetration testing means staging real attacks on AI systems. The goal is to find flaws before crooks do. Some people call it AI red teaming. Others call it AI security testing. It covers large language models (LLMs), machine learning models, AI-powered apps, generative AI tools, and the tech stack behind them.

Old-style pen testing looks at networks, web apps, and normal software bugs. AI pen testing looks at risks unique to machine learning. How does the model read your input? How does it treat your data? How can someone push it to act in ways you never planned?

AI penetration testing covers several layers:

• Model Security: Test the AI model itself. Look for prompt injection, jailbreaking, adversarial inputs, and model extraction attacks.
• Data Security: Check the safety of training data, embeddings, vector databases, and retrieval-augmented generation (RAG) systems.
• Infrastructure Security: Review the cloud setups, APIs, and links that host and connect AI systems.
• Supply Chain Security: Look at third-party models, plugins, frameworks, and code you depend on. Any of them can be a way in.
• Governance and Access Controls: Test log-in rules, permission systems, and the policies you enforce around AI use.

How AI Penetration Testing Differs from Traditional Approaches

Old-style pen testing follows well-worn steps. Testers hunt for known weak spots, bad settings, and code bugs. That still works for normal IT. It falls short on AI systems, for three reasons.

First, AI systems are probabilistic, not fixed. Normal software gives the same output for the same input every time. A large language model may not. Its answer shifts with training, temperature settings, and context.

So a tester cannot just run a set script. They must probe, adapt, then probe again.

Second, the attack surface is much wider. Attacks can arrive through the data used to train a model. They can arrive through the prompts your users type. They can arrive through the plugins and tools an AI agent touches.

They can even arrive through the embeddings and lookups that feed the model. Each path needs its own test method.

Third, AI flaws often look like odd behavior, not a broken system. A prompt injection may push an AI helper to leak private data. It may push the helper to take an action you never approved.

No error shows up. No alert fires. To catch that, a tester must know how the model should act and spot small drift.

Carnegie Mellon's Software Engineering Institute compared cyber red teaming with AI red teaming. AI red teaming borrows plenty from old-style security testing. It also calls for its own skills in machine learning, natural language processing, and adversarial AI. People still shine at complex, context-driven flaws that tools miss. AI-assisted testing brings the scale to find weak spots across huge input ranges.

The AI Threat Landscape: What You Are Up Against

The OWASP Top 10 for Large Language Model Applications

The OWASP Foundation is known for its web app security standards. It has now built a framework for AI risks. The OWASP Top 10 for LLM Applications 2025 draws on more than 100 industry experts. It is the clearest guide to AI security risks we have today.

You need to know these ten if you run or buy AI penetration testing. Here is the 2025 list.

• LLM01: Prompt Injection — This is still the most common and most dangerous AI flaw. An attacker feeds input that overrides what the model was told to do. Direct injection hides the orders in a user prompt. Indirect injection plants them in outside data the model reads. The EmailGPT flaw (CVE-2024-5184) is a real case. Attackers could push an AI email helper to reach sensitive data. Multi-language encoding, Base64 obfuscation, and emoji-based attacks have all slipped past prompt filters.
• LLM02: Sensitive Information Disclosure — AI systems can leak private data in their answers. That includes training data you own, personal details, and trade secrets. The 'poem forever' flaw proved the point. Repeated prompting pulled training data out of a model, including other users' information.
• LLM03: Supply Chain Vulnerabilities — The AI supply chain adds fresh risk. It comes from pre-trained models, third-party plugins, and training data sources. The Shadow Ray attack on the Ray AI framework is one real case. Attacks through the PyPi package registry are another.
• LLM04: Data and Model Poisoning — Attackers can taint training data or fine-tuning sets. That plants back doors, bias, or flaws inside your model. The PoisonGPT attack showed how tampering could slip past safety features on major model repositories.
• LLM05: Improper Output Handling — AI output often flows into back-end systems or straight onto a page. If nobody cleans it first, it can carry cross-site scripting, SQL injection, and other classic attacks. The AI becomes the delivery route.
• LLM06: Excessive Agency — Agentic AI lets models use tools and act on their own. That makes loose permissions far riskier. An AI agent with too much power can be steered into acts you never approved.
• LLM07: System Prompt Leakage — Careful queries can pull out your system prompts. Those prompts may expose business logic, security controls, and config details.
• LLM08: Vector and Embedding Weaknesses — Retrieval-Augmented Generation (RAG) systems link LLMs to outside knowledge bases. Weak embeddings and vector databases can leak private data. They can also let one user reach another user's content.
• LLM09: Misinformation — This used to be called 'Overreliance.' It covers AI that sounds sure but gets facts wrong. The Air Canada chatbot case shows the risk. The court held the company liable for wrong information its AI gave out.
• LLM10: Unbounded Consumption — This was once called Denial of Service. It now also covers resource limits and surprise costs. Attackers can drive an AI system to burn huge amounts of compute.

The Business Impact of AI Vulnerabilities

AI security failures cost more than a normal breach. IBM's 2025 research bears this out. Firms that reported an AI-related breach faced much higher damage. The bill climbed highest where controls were missing.

Shadow AI is the use of unapproved AI tools by staff. It adds $670,000 to breach costs on average. One in five firms reported a breach tied to shadow AI. Only 37% have policies to manage or detect it.

So the risk runs two ways. Attackers target the AI you approve. Your staff quietly use tools you cannot see.

Healthcare pays the most per breach at $7.42 million. Financial services follow at $5.56 million. Healthcare also takes the longest to spot and contain a breach. The average is 279 days. That is more than five weeks past the global average of 241 days.

These fields hold prized data, face strict rules, and detect slowly. For them, strong vulnerability management is a must.

Regulators keep pushing. U.S. agencies issued 59 AI regulations in 2024. That is more than double the year before. Across 75 countries, AI legislation rose 21%.

Among breached firms, 32% paid fines. Of those fines, 48% topped $100,000 and a quarter topped $250,000.

AI Penetration Testing Methodologies and Frameworks

Established Frameworks for AI Security Testing

Good AI penetration testing needs structure. Machine learning behaves in its own way, so the method must match. Several frameworks now guide this work.

MITRE ATLAS (Adversarial Threat Landscape for AI Systems): MITRE ATT&CK maps attacker behavior for normal cybersecurity. ATLAS does the same job for AI. It holds a knowledge base of adversary tactics, techniques, and case studies. It sorts attacks across the whole AI life cycle. That runs from first recon through model compromise and data theft.

Its tooling is less mature than ATT&CK. Even so, it is a strong start for AI red teaming.

NIST AI Risk Management Framework: The National Institute of Standards and Technology (NIST) has built broad guidance for AI risk. That includes the NIST AI RMF 600-1 profile for generative AI. It gives you a clear way to find, judge, and cut risk across an AI system's life.

OWASP GenAI Security Project: OWASP offers far more than the Top 10 list. It keeps a large set of AI security resources. You get red teaming methods, solution references, and guidance for agentic AI apps. The project also released the OWASP Top 10 for Agentic AI Applications. That one targets systems that act on their own and use tools.

Japan AI Safety Institute Framework: Japan's AISI wrote the Guide to Red Teaming Methodology on AI Safety. It lays out detailed steps for testing models in high-risk fields like healthcare and finance. It stresses steady testing before and after launch. It also calls for adversarial scenario work and help from domain experts.

The AI Penetration Testing Process

A full AI penetration test follows five phases. Each one takes a proven testing idea and fits it to AI.

1. Scoping and Information Gathering — The job starts with the AI system itself. You map its design, what it can do, and what it connects to. That means model types, training data sources, deployment setups, APIs, plugin ecosystems, and intended uses. If your AI program is mature, this phase may also review your governance policies and access controls.
2. Threat Modeling — Next, testers build threat models for that exact system. They map likely attack paths to the OWASP Top 10 for LLMs and MITRE ATLAS. They rank risks by business impact. They pick out the attack scenarios that fit the system's exposure and value.
3. Testing Execution — This phase blends manual and automated work. Manual red teaming is hands-on. Experts who know AI behavior probe for context-driven flaws. Automated testing uses special tools and even adversarial AI models. Those tools fire off thousands of test cases and surface weak spots and edge cases at scale.
4. Analysis and Reporting — Findings go into a clear report. It spells out each flaw, the likely business impact, and ranked fixes. For AI systems, the report often covers both technical controls and governance gaps.
5. Remediation Support and Retesting — Once you apply fixes, follow-up testing checks the work. It confirms the flaws are gone and that no new ones crept in. AI systems shift fast, so this phase may also suggest ongoing watch and repeat checks.

Manual vs. Automated Testing: Finding the Right Balance

The best AI penetration testing programs blend people and tools. Each side brings something the other lacks.

Manual Testing Strengths: People are creative. They chain attacks, weigh business context, and draw on instinct and domain knowledge.

Research in the International Journal of Science and Research Archive compared both sides. AI-powered tools win on speed and scale. Human testers still win on complex system defects, business logic errors, and human-focused flaws. You need people for context switching attacks, social engineering, and fresh methods that match no known pattern.

Automated Testing Strengths: AI-powered tools can try thousands of attack variants. A person tries a handful in the same time. Tools excel at weak spots in the numbers, at boundary conditions, and at steady coverage across huge input ranges.

Automated testing rose 2.5x in 2024. It is now key to covering modern AI deployments. Tools built on reinforcement learning, genetic algorithms, and adversarial AI surface edge cases that would take people weeks to find.

The Hybrid Approach: Strong providers use tools for broad coverage and baseline testing. They save human experts for deep dives on high-risk areas and fresh attack ideas. Treat AI security the same way. Let automation handle volume. Let people supply judgment and direction.

Key Attack Techniques in AI Penetration Testing

Prompt Injection and Jailbreaking

Prompt injection is the most common and most dangerous class of AI flaw. Testers use several techniques to bend model behavior.

Direct Prompt Injection: These attacks put harmful orders straight into user input. Testers try role-play prompts ('Pretend you're an AI without restrictions'). They try hypothetical framing ('In a fictional scenario where safety wasn't a concern...'). They try instruction hijacking ('Ignore previous instructions and...'). They also use context switching to shift the chat and slip past guardrails.

Indirect Prompt Injection: Smarter attacks hide orders in outside content the AI will read. Document injection buries prompts in files the AI will review. Web content injection plants them on sites the AI may fetch. RAG poisoning taints the knowledge bases that feed AI answers.

Evasion Techniques: When simple attacks fail, testers get creative. They try multi-language encoding, Base64 obfuscation, Unicode tricks, character swaps, and even emoji-based orders. The rule is simple. Any filter you can encode around or hide from can be beaten.

Adversarial Attacks and Model Manipulation

AI penetration testing goes deeper than prompts. It also probes how models read data and reach decisions.

Adversarial Inputs: These are crafted inputs that make a model read the world wrong. For image recognition, tiny pixel edits can be invisible to you yet flip the output. For language models, adversarial inputs may exploit quirks in tokenization or attention.

Model Extraction: Attackers query a model over and over. They use the answers to train a copy. That steals both the work you put into the model and any sensitive data baked into its weights.

Data Extraction and Inference: Membership inference attacks reveal whether a given record was in the training set. That can expose private facts. Training data extraction attacks go further. They pull real training examples out of model output. For models trained on sensitive data, that is a grave risk.

Supply Chain and Infrastructure Attacks

AI systems rest on long supply chains. Each link is a way in.

Pre-trained Model Compromise: Models pulled from public repositories may hide back doors, bias, or flaws. The PoisonGPT demo showed the danger. Attackers could alter models, upload them to major platforms, and slip past security checks.

Plugin and Tool Exploitation: An AI agent inherits the security of each tool and plugin it uses. A flaw in a plugin can bend AI behavior. It can also open systems the AI is allowed to reach.

Infrastructure Attacks: Cloud platforms, APIs, and dev setups behind AI are all targets. The Shadow Ray attack proved it. Flaws in AI infrastructure frameworks could be exploited at scale, hitting many firms at once.

Building an AI Penetration Testing Program

Establishing AI Security Governance

AI penetration testing works best inside broader AI security governance. IBM found that 63% of breached firms had no AI governance policies. Testing alone cannot close that gap.

Your governance needs these five pieces:

• AI Inventory and Classification: Keep a full list of each AI system you run. Include shadow AI, not just the tools you approve. Rank each one by risk level and data sensitivity.
• Access Control Policies: Set who may deploy, change, and use each AI system. Cover human users and automated agents alike.
• Data Governance: Say what data may train a model. Say how you guard sensitive data. Say how you watch data as it moves through AI systems.
• Acceptable Use Policies: Set limits on AI behavior. Name banned activities, output limits, and escalation steps.
• Regular Audit Processes: Among firms with governance policies, only 34% audit for unsanctioned AI on a regular basis. That gap leaves most of them blind to shadow AI.

Want help building AI governance? A virtual CISO brings both deep classic security skill and fresh AI risk knowledge.

Integrating AI Testing into Security Programs

Do not treat AI penetration testing as a side project. Fold it into the security testing you already run.

Continuous Testing Model: Legacy models treat pen testing as a yearly compliance box. AI systems change far too fast for that. Move to steady testing that rides your CI/CD pipeline.

Trigger a check when a model is updated. Trigger one when a new integration ships. Trigger one when training data or system design shifts.

Risk-Based Prioritization: Not all AI systems need the same depth. A customer-facing AI app that handles sensitive data needs far more scrutiny. A small internal tool needs less. Spend your testing budget where business impact, data sensitivity, and threat odds are highest.

Pre-Deployment and Post-Deployment Testing: Test before an AI system goes live. Then keep testing after. Pre-deployment testing catches flaws before anyone can abuse them. Post-deployment testing finds issues that surface from real use or a shift in the threat landscape.

Integration with Security Operations: Feed test findings straight into security operations. Use them to shape monitoring rules, detection logic, and incident response. If you rely on managed security services, check that your provider can detect and answer AI threats.

Selecting AI Penetration Testing Partners

AI security testing is a specialty. Not all providers can do it well. Weigh these five points when you choose:

• AI-Specific Expertise: Look for proven work with AI and machine learning systems. Your partner should know model designs, training processes, and AI-specific attack techniques.
• Framework Alignment: Providers should work with proven frameworks. Those include OWASP Top 10 for LLMs, MITRE ATLAS, and NIST AI RMF.
• Combined Capabilities: The best AI security testing blends manual red teaming with automated tools. Providers should be strong at both.
• Business Context: Technical testing is only half the job. Partners should grasp your industry, your legal duties, and your business goals. Then their advice can be put to work.
• Remediation Support: Finding flaws helps only if you can fix them. Look for partners who guide and support you through the whole fix.

The ROI of AI Security Testing

Quantifying the Business Case

The money case is clear. Astra Security reports that automated penetration testing saved firms $1.68 billion in 2024. Manual pentests prevented another $21.8 million in targeted risk. Together, proactive testing headed off more than $2.88 billion in losses.

The math is simple for one company. Global breach costs average $4.44 million. U.S. costs average $10.22 million. Stop one breach and you repay a whole year of testing many times over. Industry estimates put the return at up to $10 saved for every $1 spent on penetration testing.

Some firms use AI and automation across their whole security operation. They save $1.9 million per breach on average. They also cut the breach lifecycle by 80 days. Faster detection, quicker response, and early flaw discovery stack up. The gains reach well past avoided breach costs.

Beyond Cost Avoidance

Money is only part of the story. AI security testing pays off in four more ways.

Regulatory Compliance: The U.S. issued 59 new AI regulations in 2024 alone. Across 75 countries, AI legislation grew. Testing early helps you keep up. Frameworks such as CMMC 2.0, HIPAA, and PCI DSS now expect proof that you test new tech, AI included.

Customer Trust: AI now shapes the buyer experience. A security incident in an AI system can hurt your brand badly. Show solid testing practice and you build trust with buyers, partners, and stakeholders.

Competitive Advantage: Firms that ship AI safely move faster than rivals stuck on security worries. Testing clears risk out of the way. That lets you innovate.

Insurance Benefits: Cyber insurers now ask for proof of security testing before they write a policy. A documented testing program can win you better terms and lower premiums.

Emerging Trends and Future Directions

The Rise of Agentic AI Security

OWASP recently released the OWASP Top 10 for Agentic AI Applications. That signals a real shift in AI security. A chatbot just answers questions. An agentic AI system uses tools on its own. It reaches outside resources, runs multi-step workflows, and takes actions with real consequences.

That freedom brings new risk. An agent can be steered into banned actions. It can be steered into private resources. It can be steered into chaining tools in ways nobody planned.

So testing must cover more than the model. It must cover each tool, API, and resource the agent can touch.

Gartner predicts a jump by 2028. Multi-agent AI in threat detection and incident response will rise from 5% to 70% of AI applications. Most of it will assist staff rather than replace them. So security teams must guard whole systems of agents that work together. Each agent brings its own powers and permissions.

AI-Powered Security Testing

There is irony here. AI is becoming one of the best tools for testing AI. Multi-agent penetration testing systems now rival commercial products. Open-source frameworks match them on performance and stay reproducible for research.

Research published in 2025 recorded success rates above 80% on certain flaw types. Server-side template injection and broken function-level authorization stood out. These systems test at a speed and scale no human team can match.

Microsoft's PyRIT (Python Risk Identification Tool) is one example. It and similar frameworks put AI red teaming within reach of far more firms. As these tools mature, AI security testing will grow more systematic, repeatable, and scalable.

Evolving Regulatory Landscape

Rules for AI security are moving fast. The EU AI Act is one driver. The U.S. Executive Order on Safe, Secure, and Trustworthy AI is another. Many sector rules add duties for AI security testing and governance too.

Expect more rules, not fewer. Watch for growth in these five areas:

• Documented security testing of AI systems before deployment
• Ongoing monitoring and assessment duties
• Incident disclosure duties for AI security events
• Third-party audit and certification requirements
• Liability frameworks for harms caused by AI

Invest in AI security testing now. You can meet these new rules while rivals scramble to catch up.

Taking Action: Your AI Security Roadmap

Immediate Steps for Any Organization

Wherever you sit on the AI curve, take these five steps now:

1. Inventory Your AI Assets: List each AI system in use. Include the shadow AI tools your staff may have picked up. You cannot guard what you do not know about.
2. Assess Current Governance: Weigh your AI governance policies against the OWASP Top 10 for LLMs. Find the gaps in access controls, data governance, and security testing.
3. Prioritize High-Risk Systems: Spot the AI systems that handle sensitive data, talk to buyers, or make weighty decisions. Test those first.
4. Establish Baseline Controls: Put basic AI access controls in place. Add log-in checks for AI APIs, input and output filtering, and watch on AI activity.
5. Plan for Testing: Draw a roadmap for AI security testing. Cover first checks of top-priority systems and ongoing tests tied to your development lifecycle.

Building Long-Term Capabilities

Lasting AI security comes from skills that grow with the tech:

• Develop Internal Expertise: Train security teams on AI-specific flaws and testing techniques. Mix your data science and security people.
• Establish Partnerships: Build ties with security providers who specialize in AI testing. AI security is hard, so outside skill helps even strong in-house teams.
• Implement Continuous Monitoring: Put tools and processes in place to watch AI behavior, data flows, and odd security signals.
• Stay Current: The AI security landscape shifts fast. Track new threats, new frameworks, and better practice through industry work and steady learning.
• Measure and Improve: Set metrics for how well your AI security program works. Use them to drive steady gains.

Securing the AI-Powered Future

AI system penetration testing is more than a technical upgrade. It marks a shift in how you must think about risk.

The numbers are blunt. 97% of AI-related breaches happen where proper controls are absent. 63% of firms have no governance policies. The cost of failure keeps climbing.

The upside is just as big. Treat AI security testing as a strategic skill, not a compliance box, and you gain ground. You can ship AI with confidence. You can meet new rules. You can earn the trust that makes AI pay off.

The path forward is clear. Set up governance frameworks. Build technical skill. Fold security testing into how you build AI. Then partner with experts who grasp both the promise and the risk.

AI is not going away. It sits closer to the core of business every day. The winners will be the firms that harness its power and manage its risks. AI penetration testing is a key tool for that job.

Ready to check your AI security posture? Connect with an expert to see how full security testing can guard your AI investment and let you innovate with confidence.

Frequently Asked Questions

What is the difference between AI penetration testing and traditional penetration testing?

Traditional penetration testing targets network infrastructure, web apps, and normal software flaws. It uses well-known methods. AI penetration testing targets risks unique to machine learning. Those include prompt injection, model extraction, data poisoning, and adversarial attacks.

AI systems are probabilistic, not fixed. Their attack surface needs new test methods. Their flaws often show up as odd behavior rather than a broken system.

How often should organizations conduct AI penetration testing?

AI systems change fast, so a yearly test is not enough. Use steady testing tied to your development cycle. Trigger a check when models are updated, new integrations ship, or anything major shifts.

At a minimum, test high-risk AI systems each quarter. Those are the ones that handle sensitive data or talk to buyers. Run a full assessment once a year. Add spot checks after any big change.

What are the most common AI security vulnerabilities found during testing?

The OWASP Top 10 for LLMs 2025 names the leaders. Prompt injection tops the list, where crafted inputs bend model behavior. Next comes sensitive information disclosure, where the model leaks private data. Supply chain vulnerabilities follow, driven by third-party models and plugins.

Improper output handling ranks high too, since AI output often skips proper validation. Tests also turn up missing access controls, weak governance policies, and shadow AI use.

Can automated tools replace human expertise in AI security testing?

No. The strongest AI penetration testing blends tools and people. Tools bring scale. They sweep huge input spaces and find weak spots fast.

People bring creativity, business context, and the instinct to spot complex flaws. The hybrid approach uses automation for broad coverage. Human experts then focus on high-risk areas and fresh attack ideas.

What frameworks should guide AI penetration testing programs?

Start with the OWASP Top 10 for LLM Applications 2025. It is the clearest guide to AI flaws. Add MITRE ATLAS for adversary tactics and techniques against AI. Use the NIST AI Risk Management Framework for structured risk work.

For systems that act on their own, add the OWASP Top 10 for Agentic AI Applications. Your testing provider should know all four and adapt them to your setting.

How do I know if my organization needs AI penetration testing?

If you use AI at all, you likely need AI security testing. It is urgent when your AI handles sensitive customer data or faces outside users. The same goes if you lean on third-party AI models or plugins. Missing AI governance policies, unknown shadow AI, and rules that reach AI systems all raise the stakes.

98% of firms use AI, but only 66% test their systems on a regular basis. Most have real gaps to close.

What is the cost of AI penetration testing?

Cost depends on scope, complexity, and depth. Most first engagements open with threat modeling and an architecture review. Testing then targets your top-priority systems.

For context, firms that use AI and automation across security operations save $1.9 million per breach. Industry estimates suggest $10 saved for every $1 spent on penetration testing. Breach costs average $4.44 million globally and $10.22 million in the United States, so the return is strong.

How can organizations address shadow AI risks?

Shadow AI is unapproved AI tool use. It adds $670,000 to breach costs on average. Fixing it takes several moves at once:

• Deploy discovery tools that reveal AI use across the company.
• Write clear acceptable use policies.
• Offer approved tools that meet real staff needs.
• Add technical controls that detect and block unapproved AI access.
• Build a culture where staff know the risks and the proper route to adopt AI.

Related reading

• Web Application Penetration Testing: Protecting Your Most Exposed Assets
• Black Box vs. White Box vs. Gray Box Testing: Which Is Right for You?
• What is Penetration Testing? A Plain-English Guide for Business Leaders