Attack Surface Management for AI: Securing Expanding Digital and AI Assets

In the 21st-century digital economy, Artificial Intelligence (AI) is no longer a “niche” technology, available only to businesses with deep pockets and/or vast technical know-how. Today, millions of firms around the world are able to leverage AI to automate repetitive tasks and streamline processes in a range of business functions.

AI enables companies to boost human productivity and efficiency and reduce human errors. It also enhances workplace communication, accelerates innovation, and boosts business profitability. In fact, AI has made such deep inroads into the business landscape that it is now widely considered a critical driver of business success.

However, there’s also a flip side to the AI era: it expands your organization’s attack surface.

Cyberattacks against AI agents, models, and tools are increasing in both frequency and scale. To mitigate the risk against AI assets and secure your fast-growing “non-traditional” attack surface, it’s critical to take proactive and continuous action to discover and classify AI assets, identify and prioritize their vulnerabilities, and execute remediations.

This is where Attack Surface Management(ASM) plays an important role.

Common Threats to Enterprise AI Systems

AI tools, models, plugins, functions, gen AI prompts, and of course, data, are all potential entry points that threat actors may target. The manipulation of these new attack surfaces can result in significant security risk for organizations, and unfortunately, adversaries already have a range of techniques at their disposal to attack these high-value AI assets.

They can poison an AI model’s training data with malicious or biased data points to manipulate its decisions and output. Or they may execute prompt injection attacks targeting AI agents and large language models (LLMs). These attacks can enable them to conduct reconnaissance of enterprise systems, manipulate business processes, and exfiltrate sensitive data. Attackers could even move laterally within the network to hijack even more systems and compromise even more business-critical information assets.

Threat actors may also abuse AI agent tools through cleverly crafted prompts. If an AI agent has access to numerous enterprise tools and workflows, the “right” prompt could get it to perform a host of unauthorized or dangerous actions, such as extracting sensitive data, deleting cloud resources, sending out phishing emails, or approving fraudulent transactions. Agentic AI is expected to be part of 33% of software applications and autonomously make 15% of day-to-day work decisions by 2028, making the potential for abuse of these systems a serious concern for modern organizations.¹

Model inversion attacks are another persistent risk to AI systems. According to OWASP, these attacks occur when “an attacker reverse-engineers the model to extract information from it”.² Such attacks may result in confidential information about the input data being compromised or stolen.

Most of these attacks allow threat actors to influence the capabilities of an AI system, and ultimately, control its outcomes. This can cause serious damage to the target organization by way of data theft, financial losses, reputational damage, and legal or regulatory challenges.

So how can you remediate these threats to your AI systems and safeguard your operations, finances, and reputation? Attack surface management for AI may be the answer.

Mitigating AI Risk with Attack Surface Management for AI

Attack Surface Management (ASM) for AI is the continuous and proactive process of discovering and analyzing all the ways an AI system can be attacked, and implementing controls to protect all core AI attack surfaces. It involves prioritizing risks to all AI assets at their most critical attacker entry points, including:

• Prompts
• User interfaces
• AI models
• Deployment pipelines
• AI agents
• APIs; and
• Data

Last but definitely not least, an ASM for AI strategy involves taking deliberate and proactive action to reduce exposure and minimize the number of AI targets that real attackers could potentially reach.

How Attack Surface Management Works for AI Assets

Similar to the way ASM is used with other systems, ASM for AI starts with the discovery and evaluation of AI assets and attack vectors. ASM platforms ease asset discovery, inventory, and classification. They identify exposed assets and clearly show their critical attacker entry points, thus providing full visibility into all AI attack surfaces.

Vulnerability identification is another important aspect of ASM for AI. Unified ASM tools identify vulnerabilities within AI systems. Additionally, they analyze potential attack vectors based on analyses of real attackers’ Tactics, Techniques, and Procedures (TTPs) to demonstrate the real-world importance of those weaknesses.

ASM for AI also includes risk analysis. After identifying vulnerabilities in AI systems, the ASM platform prioritizes them based on their severity and potential impact. This enables security teams to narrow down the top remediation priorities – and assign resources accordingly.

ASM for AI provides real-time results of risk exposure, plus actionable, contextual remediation recommendations. These insights enable security personnel to make optimal decisions about which controls to implement in order to harden security defenses, reduce AI risk, and safeguard AI assets and data from attack.

Safeguard Your Expanding Digital and AI Assets with BreachLock

To effectively mitigate AI risks and ensure these technologies continue to deliver value with minimal risk, security teams need to move beyond static security testing with an offensive mindset.

BreachLock’s suite of offensive security tools and services is designed to discover, prioritize, and validate the most exploitable points of interest to an attacker, including vulnerabilities specific to AI systems. Whether you’re 100% in-house, expert-led pentesting with a Penetration Testing as a Service (PTaaS) delivery model, or you need to validate your defenses against agentic AI threats using our agentic AI-powered Adversarial Exposure Validation(AEV) tool for autonomous penetration testing, BreachLock provides the contextual insights you need to safeguard your systems from adversaries where it matters most.

By aligning these capabilities with Continuous Threat Exposure Management (CTEM), we ensure your AI attack surface is continuously discovered, analyzed, and prioritized for remediation.

Ready to see how BreachLock safeguards AI and digital assets? Schedule a discovery call with an expert today.

References

1. Gartner (2024). Intelligent Agents in AI Really Can Work Alone. Here’s How.
   https://www.gartner.com/en/articles/intelligent-agent-in-ai
2. OWASP (2023). ML03:2023 Model Inversion Attack.
   https://owasp.org/www-project-machine-learning-security-top-10/docs/ML03_2023-Model_Inversion_Attack

About BreachLock

BreachLock is a global leader in offensive security, delivering scalable and continuous security testing. Trusted by global enterprises, BreachLock provides human-led and AI-powered Attack Surface Management, Penetration Testing as a Service (PTaaS), Red Teaming, and Adversarial Exposure Validation (AEV) solutions that help security teams stay ahead of adversaries.

With a mission to make proactive security the new standard, BreachLock is shaping the future of cybersecurity through automation, data-driven intelligence, and expert-driven execution.

Author

BreachLock Labs