Genai Security Risks and Best Practices

Genai security risks are a serious concern, especially when it comes to data breaches. According to research, 75% of genai systems have experienced a data breach within the past year.

One of the primary risks is unauthorized access to genai data. This can happen when passwords are weak or easily guessable, allowing hackers to gain access to sensitive information.

Genai systems are also vulnerable to phishing attacks, which can trick users into revealing their login credentials. In fact, 90% of genai security breaches are caused by phishing attacks.

To mitigate these risks, it's essential to implement robust security measures, such as multi-factor authentication. This can include using a combination of passwords, biometric data, and one-time passwords to verify user identity.

GenAI security risks are a growing concern as these technologies become increasingly integrated into our daily lives. The majority of cyber security risks associated with GenAI stem from how AI models are integrated into systems and workflows rather than from the models themselves.

GenAI systems with excessive agency can be manipulated by attackers, causing the system to execute malicious actions and posing significant security risks. This can happen via jailbreak and prompt injection attacks.

The convenience and accessibility of GenAI tools can lead to new challenges that conventional security tools are not designed to handle. GenAI tools can boost productivity, but they also add exponential risk to our systems.

Prompt injections can confuse AI-powered agents, potentially compromising large language model (LLM) powered AI applications. This is an area that security experts are actively exploring to identify potential vulnerabilities.

To mitigate these risks, it's essential to address the practical risks associated with integrating GenAI into enterprise systems and workflows. This can be achieved by creating a shared risk understanding between development teams, cyber security, and business units.

Having an effective AI governance strategy is vital for trusted AI. This includes involving data scientists and engineers, data providers, specialists in diversity, equity, inclusion, and accessibility, user experience designers, functional leaders, and product managers.

Defining the AI adoption objectives and acceptable use cases is a crucial part of AI governance. Adapting or creating ad-hoc risk management frameworks based on your organization's needs and regulatory requirements is also essential.

Customers can control their data with enterprise-grade capabilities, such as data isolation, data protection, and compliance support.

Market consolidation is a significant milestone in the AI market, and it's a turning point that will have far-reaching consequences.

Threat actors will start launching attacks once the market matures to the point where a single technology dominates a 50% market share or when three or fewer technologies corner the market.

Before investing in plans and infrastructure, threat actors want assurances of ROI, and the current market landscape with too many GenAI tools and platforms spread across too many companies doesn't provide that.

The market needs to narrow for GenAI attacks to become cost-effective for cybercriminals, and that's exactly what will happen when the market consolidates.

Without ubiquity, attacks cost too much time and money, which is why threat actors will wait for the market to mature before launching attacks in earnest.

Governance is key to responsible AI adoption. Effective governance will be vital for companies to use generative AI responsibly.

Having an effective AI governance strategy will be vital, and many people inside and outside of your organization can influence your ability to use generative AI responsibly. This includes data scientists and engineers, data providers, specialists in diversity, equity, inclusion and accessibility, user experience designers, functional leaders, and product managers.

Defining the AI adoption objectives and acceptable use cases is crucial for governance. Adapting or creating ad-hoc risk management frameworks based on your organization's needs and regulatory requirements will also be essential.

A nimble, collaborative, regulatory-and-response approach is emerging with generative AI, requiring compliance officers to keep up with new regulations and stronger enforcement of existing regulations. This may require a major adjustment for compliance officers.

Here are some key roles that will be involved in governance:

Without proper governance and supervision, a company's use of generative AI can create or exacerbate legal risks, including lax data security measures and inaccurate outputs. To challenge and defend GenAI-related issues, legal teams will need a deeper technical understanding.

As a CISO, you need to be aware of the increased risk of sophisticated phishing attacks that can be launched using generative AI.

Generative AI can create compelling, custom lures in chats, videos, or live-generated "deep fake" video or audio, impersonating someone familiar or in a position of authority.

More threat actors will target your organization's AI systems, which can be manipulated to make incorrect predictions or deny service to customers.

To manage this risk, your organization will need stronger cyberdefense protections for proprietary language and foundational models, data, and new content.

Internal audit leaders need to create a risk-based audit plan specific to generative AI, which requires designing and adopting new audit methodologies, new forms of supervision, and new skill sets.

Auditing will be a key governance mechanism to confirm that AI systems are designed and deployed in line with a company's goals. Understanding the problem the company is trying to solve using GenAI is an important starting point.

It's difficult and ineffectual to assess the risks that generative AI systems pose independent of the context in which they are deployed.

Protecting Digital Assets involves using non-human identities (NHIs) to power business-critical applications, which has opened the doors for seamless operational efficiency. Unfortunately, these doors aren't secure, and non-human identity attacks can compromise your digital assets.

Attackers can use various routes to compromise your systems, making it essential to explore the potential attack paths through Attack Path Mapping. This exercise helps pinpoint remediation activities that yield the greatest business impact.

To simplify security, AI-assistive features can help teams proactively mitigate risk and usher in a new era of effectiveness. The Security Command Center, for example, summarizes high-priority alerts for misconfigurations and vulnerabilities, highlighting potential impact and recommending mitigations before assets can be exploited.

Generative AI is poised to transform the future of business, but it also introduces new risks. Malicious actors attempt to "jailbreak" Large Language Models (LLMs) by injecting carefully crafted prompts, tricking them into executing unauthorized actions or revealing sensitive information.

To mitigate these risks, advances in generative AI will profoundly change how practitioners across skill levels "do" security. AI-assistive features can help teams proactively mitigate risk and usher in a new era of effectiveness.

Gemini in Security Command Center summarizes high-priority alerts for misconfigurations and vulnerabilities, highlighting potential impact and recommending mitigations before assets can be exploited. This platform is specifically designed for defending against threats to your Google Cloud assets.

Gemini in Security Operations can reduce much of the repetitive work that plagues cybersecurity practitioners. It enables you to use natural language to generate queries and interact with security event data conversationally, and assists investigations by surfacing contextual information and offering recommendations for quick response.

Here are some features that can help simplify security for experts and non-experts alike:

Protecting Digital Assets from Non-Human Identity Attacks is crucial in today's digital landscape. Non-human identities (NHIs) are used to power business-critical applications, especially in cloud computing environments, but they can also be exploited by malicious actors.

Inadequate monitoring, logging, and rate-limiting mechanisms hinder the detection of malicious activity, making it challenging to identify and respond to security incidents promptly. This is a major concern, as NHIs can be used to launch attacks that might go undetected.

Gemini in Security Command Center can help mitigate this risk by summarizing high-priority alerts for misconfigurations and vulnerabilities, highlighting potential impact and recommending mitigations. This feature can help teams proactively address security issues before they become major problems.

To protect digital assets from non-human identity attacks, it's essential to implement robust security measures, such as monitoring, logging, and rate limiting. This can help prevent malicious activity and ensure the integrity of digital assets.

Here are some key considerations for protecting digital assets from non-human identity attacks:

By taking these steps, organizations can protect their digital assets from non-human identity attacks and ensure the security and integrity of their digital infrastructure.