As AI continues to permeate enterprise operations, it has also created a host of new vulnerabilities for hackers to exploit. AI is not just expanding the enterprise attack surface, it is fundamentally changing what security needs to defend.
Yet fewer than one in three businesses have basic protections like AI firewalls in place. In 2025, 36% of security and technology executives said that AI was outpacing their security capabilities, while some 90% said they lack the security standards needed to defend against present-day, AI-driven threats.
With this new class of AI threats rapidly rising, businesses are recognizing the urgency of adopting security strategies for an AI-driven world. Analysts predict that the market for AI security software will grow to $1.2 trillion by 2031, with 86% of executives planning to increase their AI security investments this year.
But adoption is only the beginning. AI security systems must be thoroughly tested through “red teaming” – simulated attacks on AI systems that help teams identify vulnerabilities and test their defences – before they are deployed in the real world.
The Evolving AI Threat Landscape
The first of the new risks brought about by AI in enterprise operations is AI’s autonomy. AI systems, especially autonomous agents, are gaining more decision-making power, and, as more capabilities exit the realm of human oversight, so do the security risks.
In addition, to fuel their heightened capabilities, LLMs and AI agents are increasingly connected to a vast ecosystem of applications, databases, and APIs, and are exposed to sensitive data, including enterprise knowledgebases, personal information, and financial transactions.
This creates a rapidly expanding attack surface and a fundamentally different kind of exposure. Traditional red teaming focuses on defending infrastructure from external attacks: servers, networks, and access controls. AI systems add an entirely new layer in the form of the models themselves, their applications, and their behavior. Vulnerabilities here aren’t found in code, they’re found in how a model interprets input, makes decisions, and responds under pressure.
Threats to AI Require AI Red teaming
Traditional red teaming was designed for deterministic environments. Applications, networks, and infrastructure behave in predictable ways: the same input produces the same output, and vulnerabilities can be reproduced and patched.
AI systems don’t behave that way. They are probabilistic and context-dependent – shaped by training data, sensitive to phrasing, and capable of behaving differently under identical inputs. A vulnerability in an AI model’s reasoning won’t show up in a port scan. You can’t find a hallucination with a penetration test.
This is why AI red teaming exists as a distinct discipline. Traditional red teaming tests whether a system can be breached; AI red teaming tests whether it can be manipulated, deceived, or coerced into undesired behavior.
In traditional environments, attackers exploit code paths. In AI systems, they exploit reasoning paths.
The attack vectors that matter in AI simply don’t exist in traditional infrastructure and traditional tools have no way to find them:
- Prompt injection: Because AI models are non-deterministic systems that “think for themselves” and therefore behave unpredictably, they are susceptible to attacks that manipulate model outputs by feeding them malicious inputs, rather than those based on set data or consistent reasoning.
- Jailbreaking: Attackers can craft inputs that override safety restrictions, causing models to reveal restricted internal content or perform unintended actions.
- Data leakage: Hackers can trick AI models into inadvertently exposing sensitive information through their outputs.
- Model misbehavior: AI models can produce biased or false results, undermining business operations and user trust.
- Agent misuse: Attackers can exploit AI agents and connected tools to execute attacks. For example, an AI agent with access to email or messaging platforms could be manipulated to generate phishing campaigns or to automate social engineering.
Failing to address these vulnerabilities due to AI can lead to severe consequences, such as reputational damage, loss of customer trust, financial costs, and even legal repercussions – due to non-compliance with regulations like GDPR and CCPA.
Successful AI Red teaming
AI system threats are typically multi-modal attacks, which evolve over multiple interactions or combine text, image, and voice inputs. More malicious and effective than single-shot attacks, these longer, conversational scenarios make LLMs more likely to reveal sensitive information or exhibit unexpected behaviors.
Red teaming must accordingly go beyond basic testing. Because these multi-modal attacks are designed to exploit how an AI model interprets and responds to human-like input, red teaming must account for the ways their contextual manipulation can create unintended or compromising outputs. Simulating these unique multi-modal attacks is key to defending against them. By testing how AI models handle different inputs (text, image, voice), AI red teaming techniques help identify weaknesses not just in infrastructure, but in how AI systems make decisions.
Effective AI red teaming relies both on AI specialists, who uncover model-specific weaknesses like training or prompt vulnerabilities, and cybersecurity experts, who assess broader attack surfaces and protect underlying infrastructure.
The Green Light for AI Red Teaming
While traditional red teaming remains valuable, it was designed for a world in which systems do what they’re told. AI has changed that. When a system can be prompted, persuaded, or tricked into acting against its own guardrails, security requires a fundamentally different approach: one that mimics not just hackers, but manipulators.
Red teaming has long been a business necessity, but the rise of a new class of threats necessitates a new era of techniques. The rapid rise of autonomous AI systems makes it critical to test AI models for vulnerabilities, before they can be exploited.
Beyond enhancing security, AI red teaming enables businesses to build customer trust by demonstrating a commitment to ethical AI usage and secure data practices. Organizations that prioritize AI red teaming won’t just protect themselves, they’ll differentiate themselves. In a market where customers, partners, and regulators are all paying closer attention to how AI is deployed, a demonstrable commitment to secure and ethical AI practices is a genuine competitive advantage, not just a compliance checkbox.
