Generative AI is revolutionizing industries, creating unprecedented opportunities—and equally unprecedented risks. Whether you’re a security leader, engineering leader, or developer, you’ve likely encountered new challenges in protecting sensitive data, maintaining compliance, and embedding security into AI workflows.
We think the latest Gartner® report, "Emerging Tech: Secure Generative Communication for LLMs and AI Agents," dives into these pressing issues and offers actionable insights tailored to your needs.
For Security Leaders: Mitigating Risk in Generative AI Workflows
The adoption of AI systems introduces unique vulnerabilities like prompt injection attacks and data leakage risks. For security leaders, the stakes are high:
Challenge: How do you protect sensitive data exchanged in real-time communication between AI systems?
Challenge: Can you ensure compliance with data protection regulations while scaling AI deployments?
“Organizations must implement robust security measures to protect AI systems from prompt injection attacks, which can manipulate AI behavior and compromise data integrity.” The report outlines:
Near-term implications and recommended actions for security compliance standards
Strategies to safeguard sensitive information in LLM workflows.
Best practices for securing data pipelines against evolving threats.
How to implement scalable security frameworks that adapt to AI-driven innovation.
By leveraging these insights, security leaders can minimize risks while maintaining trust and compliance.
For Engineering Leaders: Building Fast and Secure Applications
Engineering leaders are under constant pressure to deliver innovative applications at speed—but innovation must not come at the cost of security. Key pain points include:
Challenge: Balancing fast feature deployment with robust security practices.
Challenge: Ensuring that AI-powered applications comply with regulatory requirements set by security leaders without slowing down delivery.
“Deploying security services across multiple points in the AI data pipeline is essential to mitigate risks such as data leakage and poisoning.” For engineering leaders, this means:
Embedding security seamlessly into workflows without compromising speed.
Leveraging composable, API-driven solutions to simplify integration.
Reducing vulnerabilities in real-time AI communication and data ingestion.
These actionable steps allow engineering leaders to align security and development priorities effectively.
For Developers: Simplifying Security in AI Development
Developers often face the dual challenge of building cutting-edge applications while ensuring they’re secure. Common frustrations include:
Challenge: How do you implement robust security measures without adding complexity?
Challenge: Can you build secure AI applications without impacting performance?
“Ensuring compliance with data protection regulations is critical when deploying AI models, necessitating tools that can identify and redact sensitive information.” For developers, this translates to:
Leveraging pre-built security tools like APIs to simplify implementation.
Automating the identification and removal of sensitive data in generative workflows.
Building secure-by-design applications with minimal disruption to your processes.
By following these recommendations, developers can focus on innovation while ensuring robust security.
Why We Feel This Gartner® Report Matters
Generative AI workflows are complex, but securing them doesn’t have to be. We think the Gartner® Report, "Emerging Tech: Secure Generative Communication for LLMs and AI Agents" equips you with:
Insights to mitigate the unique risks of AI adoption.
Strategies to align security, compliance, and innovation.
Best practices for embedding security at every stage of development.
Whether you’re leading security efforts, driving engineering innovation, or building applications, this report provides the guidance you need to protect your systems and stay ahead in the rapidly evolving AI landscape.
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Gartner Compliance Attribution
Gartner, Emerging Tech: Secure Generative Communication for LLMs and AI Agents, By Evan Zeng, Lawrence Pingree, 12 June 2024
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