Securing AI Sucks
78% of CISOsWe analyzed over 1,500 discussions among CISOs and security specialists through cybersecurity forums and surveys we conducted, and found a common theme: securing AI systems is not only complex but also risky.
Securing AI is critical. Here's why ignoring it is risky
An engineer deploys an AI model
They notice vulnerabilities like data leakage, adversarial attacks, and unauthorized access compromising the AI system.
They attempt to implement security measures
They try various methods like encryption, access controls, and manual code reviews to secure the AI model.
Complexity increases, risks multiply
Despite their efforts, new security issues emerge. The engineer spends countless hours patching vulnerabilities, but the risks continue to grow.
The project faces serious consequences
Without effective security, the AI system becomes a liability, leading to data breaches, loss of customer trust, legal penalties, and even potential harm to users.
Findings from CISO’s
The reasons why securing AI sucks
88%
are Concerned or
78%
Disagree or Strongly Disagree that traditional security tools are sufficient for addressing AI-specific vulnerabilities
80%
find identifying and monitoring AI applications Challenging or Extremely Challenging
85%
Most CISOs face substantial challenges when integrating AI security measures into their systems.
“AI's unpredictability is a major risk, we can't secure what we can't foresee..”
"Traditional tools fail against AI-specific threats; we need
"Unmonitored AI apps are a blind spot — we can't protect what we don't know exists."
"Integrating AI security into our existing framework is daunting; it's complex and resource-intensive."
Understanding the risks of inadequate AI security
Hover over the cards to see the consequence to each risk
Data breaches & privacy violations
Sensitive information exposure:
AI systems often handle personal or confidential data. A security lapse can lead to massive data breaches, exposing sensitive information to malicious actors.
Compliance issues
Violations of regulations like GDPR or HIPAA can result in hefty fines and legal action.
Adversarial attacks
Manipulation of AI Outputs:
Attackers can feed deceptive inputs to AI models, causing them to make incorrect or harmful decisions.
Undermining trust
Adversarial attacks can erode user confidence in AI systems, impacting adoption and reputation.
Intellectual property theft
Model stealing:
Competitors or hackers can replicate your AI models, stealing years of research and development.
Economic losses
AI model theft can lead to significant financial setbacks, as well as complex, costly legal battles.
Data poisoning
Corrupted training data:
Attackers introduce malicious data during the training phase, compromising the model's integrity.
Long-term damage
Detecting and rectifying poisoned data is time-consuming and costly.
Unauthorized access & control
System Hijacking:
Unauthorized users gaining control can manipulate AI behavior or shut down services.
Operational disruptions
This can lead to downtime, financial loss, and damage to critical infrastructure.
Ethical & legal implications
Bias and discrimination:
Security flaws can be exploited to introduce or amplify biases, leading to unfair outcomes.
Legal liability
Organizations may face lawsuits due to harm caused by insecure AI systems.
Real-life examples of AI security failures
When trying to make your AI behave in a certain way, AI guardrails offer a more effective, time-efficient, and intelligent approach compared to prompt engineering. Here are some real-life examples of how developers struggle with prompt engineering and how AI guardrails can provide a smarter solution.
Story 1
TechNova created a generative AI assistant for quick internal information access. An employee asked it about confidential financial projections, and the AI unintentionally shared sensitive data. This leak led to significant financial losses and reputational harm for the company.
Story 2
At SecureCorp, an employee faced challenges while writing a complex client proposal. To save time, they used an external generative AI service, inputting sensitive client information to generate content. The external AI service stored the data, which later appeared in publicly available outputs, exposing confidential client details.
Story 3
InnovateX built a homegrown generative AI tool to assist developers by generating code snippets. A developer asked the AI for help with a coding problem, and the AI included proprietary code from another project in its response. This led to internal code being exposed in unauthorized contexts.
Story 4
AlphaOmega developed a generative AI model for analyzing market trends. The model was deployed without proper authentication mechanisms. Hackers discovered the unsecured endpoint and used it to generate reports containing sensitive market analysis, which they sold to competitors.
Story 5
At DataGuard Inc., employees use an internal AI assistant to manage schedules and meetings. An employee tried to manipulate the AI assistant into revealing another executive's calendar by using a cleverly crafted prompt.
Story 6
GlobalMedia uses a generative AI tool to draft articles and marketing content. A marketer requested an article on upcoming projects, and the AI included details about unreleased products due to its training data containing sensitive internal documents.
How do AI Guardrails work?
Positioned between your homegrown AI agents, users, and third-party AI tools, guardrails act as a vital security layer, instantly reviewing every message and interaction to ensure compliance with your established rules. Whether a risky message originates from the user, the AI model, or involves third-party app usage, it is immediately blocked, overridden, or rephrased in real-time, preventing security threats before they affect your system. Safeguard your in-house AI applications and third-party AI tool integrations. Book a demo today to see how our solution protects your systems from risky interactions.
