Key Takeaways

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• AI changes everything: Attackers and defenders are both wielding AI. Success depends on mastering both sides.
• Defend your AI: Secure your AI infrastructure, APIs, and SaaS tools from data theft, model abuse, and prompt-based attacks.
• Data is the foundation: Mitiga’s Cloud Security Forensics Data Lake provides the full-fidelity context that makes AI-driven detection and response possible.
• AIDR is the future: Unified, AI-native defense across Cloud, SaaS, Identity, and AI is how modern SOCs will stay ahead of the new risk surface.

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AI is redefining the threat landscape. Attackers now weaponize AI to scale reconnaissance, automate credential theft, craft believable phishing campaigns, and even build their own malicious AI agents. Meanwhile, enterprises are racing to deploy AI applications and models, often faster than their security controls can keep up.

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This new era demands AI security solutions that not only use AI to protect the enterprise but also protect the AI infrastructure itself.

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In the first blog of this series, I explored how Mitiga’s Helios AIDR helps you defend with AI by using AI for cloud detection and response so you can augment human analysts, accelerate triage, and automate response actions.

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In this second blog, we’ll talk about how AI sits at the center of your business and why it’s now a fast-growing attack surface. And I’ll explain how Mitiga’s Helios AIDR helps you defend your AI by turning AI applications and models into observable, defensible assets across your cloud and SaaS estate – all powered by Mitiga’s Cloud Security Data Lake.

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AI Security Challenges: Cloud Sprawl and New Attack Vectors

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Modern cloud estates are stretching farther and faster than SecOps teams can manage. There’s a complexity explosion: hundreds of SaaS applications, multi-cloud infrastructure, hybrid identities (human and non-human), and now AI agents and services all weaving together. Very few organizations can reliably track relationships and permissions across this ecosystem. AI visibility gaps are no longer a minor inconvenience, they’re existential risk.

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Identity and IAM remains a core challenge: attackers are no longer fighting against the layers of your defenses, they’re just logging in. And non-human identities like service accounts, API-connected agents, and AI agents outnumber human identities and often lack strong oversight, use insecure default configurations, and offer limited log analysis. This means that threat actors can exploit permissions and configuration drift across cloud, SaaS, and identity — often without raising alarms. All of this makes token management, session monitoring, and credential hygiene essential.

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Now layer AI infrastructure on top of this sprawl. AI infrastructure like Claude or Bedrock, services like ChatGPT Enterprise, and 3rd Party embedded AI SaaS don’t just introduce new components — they introduce new attack vectors and visibility gaps that current security technologies can’t defend against. These AI services map additional relationships (who’s calling what, which model is being used where), create fresh identity surfaces, and accelerate the speed at which attackers can probe, abuse, and exfiltrate data. For security teams still relying on manual or semi-automated workflows, simply keeping up with basic asset and risk inventory has become nearly impossible.

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And in the year ahead, the next big cloud breach won’t start with a misconfigured bucket. Rather It will likely start in a Model Context Protocol (MCP) API. As businesses connect AI assistants to their enterprise resources and data, these new API layers will make highly sensitive systems open and available in ways that are still hard to predict.

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Common AI Attack Vectors: From Prompt Injection to Model Poisoning

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If you’re building or relying on AI infrastructure – whether via SaaS platforms like ChatGPT Enterprise or on-prem/managed models like Claude or Bedrock – you’re now defending a living target. Attackers are increasingly eyeing these AI SaaS platforms because they represent new, vulnerable, and under protected risk surfaces:

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• Unknown / under-tested vulnerabilities — AI platforms are still very new in enterprise usage. Misconfigurations, poor access controls, or overly permissive APIs can expose critical business logic or model data.
• Model manipulation & poisoning – Attackers could upload malicious data, poison training sets, or inject prompts to make AI act in harmful ways.
• Prompt injection & jailbreaking – As seen in Claude, threat actors tricked the AI into misclassifying malicious actions as safe by framing tasks in unsuspecting ways.
• Identity abuse – AI services are often tied to user or service accounts, and compromised credentials could give attackers direct access to powerful agents.
• Data exfiltration from AI SaaS – Because AI tools can ingest sensitive data, attackers can abuse compromised tokens to extract business-critical or proprietary information.

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These are not isolated AI issues; they span cloud, SaaS, identity, and AI infrastructure. Without deep, correlated visibility, they simply blend into background noise.

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How to Secure AI Infrastructure: Protecting ChatGPT, Claude, and Bedrock

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With the new AI attack surface, cloud defenders must ask a new set of questions: Who has access to our AI models? What guardrails are in place? What happens if an attacker jailbreaks our AI?

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Unfortunately, legacy posture management and detection and response tools weren’t designed for this. They can’t see into the usage patterns, authentication flows, or SaaS-layer data exchanges that power modern AI applications.

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Mitiga’s Zero-Impact CDR platform with Helios AIDR for AI infrastructure and services closes this gap.

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Our AI-native cloud detection and response (CDR) platform defends your AI by monitoring and securing your AI environments as first-class assets within your cloud and SaaS estate by:

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1. Detecting attacks targeting AI infrastructure (e.g. Bedrock, Vertex, Claude) and AI SaaS services (e.g., ChatGPT Enterprise, Gemini, Copilot) through contextual telemetry and behavioral analytics.
2. Seeing and securing embedded 3rd Party AI SaaS (e.g. Bedrock, Vertex, Claude) and AI SaaS services (e.g., Agentforce for Salesforce) through contextual telemetry, behavioral analytics, and identity mapping and identification.
3. Identifying AI abuse and compromised identities, revealing when users or tokens are exploited to extract sensitive data or execute malicious prompts.
4. Protecting against API and model-targeted attacks, such as prompt injection, poisoning, and data theft attempts against proprietary models or AI APIs.

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In essence, Mitiga transforms AI applications from opaque black boxes into observable, defensible components of your cloud ecosystem, delivering the same clarity and response automation that SOCs rely on for traditional workloads.

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Because defending your AI infrastructure means protecting the intelligence, data, and trust that your enterprise runs on.

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Cloud Security Data Lake: The Foundation for AI Defense

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All of this depends on one thing: high-quality, long-lived, and contextualized telemetry.

Every pillar of Mitiga’s AIDR strategy is powered by one foundational capability: our Cloud Security Data Lake that makes AI-driven detection and response genuinely intelligent.

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The forensics data lake continuously collects and normalizes logs and signals from more than a hundred cloud, SaaS, identity, and AI sources and can retain up to 1,000 days of full fidelity history.

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For AI security specifically, three aspects of the data lake are vital:

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Operational context for AI activity

Telemetry from AI platforms is enriched with identity, network, and SaaS context so Helios AIDR can separate normal AI-assisted work from activity that hints at jailbreaks, data harvesting, or account takeovers.

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Deep historical visibility into AI behavior

With more than 1,000 days of lookback, security teams can reconstruct the story of an AI incident: when an account first used a model, how prompts changed, and what other systems were touched.

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A unified data model for cloud, SaaS, identity, and AI

Because all signals live inside one correlated model, AI-specific detections can be traced directly to the workloads, applications, and people they affect.

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In short, this isn’t just more data about AI – it’s the right data, structured so Helios AIDR and other AI agents can reliably detect subtle anomalies and early-stage attacker behaviors targeting your AI infrastructure.

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Real-World AI Security Incident: Detecting Prompt Injection Attacks

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Consider a global enterprise rolling out ChatGPT Enterprise to help developers and product teams work faster. Over time, users start pasting increasingly sensitive information into prompt – architecture diagrams, runbooks, and even proprietary code.

An attacker phishes one internal user and steals their credentials. Rather than going straight for known SaaS apps, they log into the AI tenant and:

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• Craft prompts that coax the model into summarizing and re-emitting internal documents.
• Use innocuous-looking queries to aggregate information they have no business seeing.

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Mitiga Zero-Impact outcome with Helios AIDR, backed by the Cloud Security Data Lake:

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• Identity-centric analytics show that the user’s AI behavior diverges sharply from their historical baseline—different hours, different models, different data domains.
• Cross-environment correlation ties that behavior to a risky login and subtle changes in SaaS access.
• AI-aware detections flag the prompt pattern as consistent with targeted data harvesting rather than productivity use.

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The SOC doesn’t just see “busy AI.” They see a likely credential-theft-driven exfiltration path through the AI layer. Real-time response actions and playbooks can automatically revoke tokens, lock the account, and adjust tenant configuration, cutting off the attack before it becomes a breach and causes real business impact.

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Learn more about our incident response services for cloud and SaaS security incidents.

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Benefits of AI Security: Visibility, Speed, Accuracy, and Zero-Impact Breach Prevention

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By bringing AI infrastructure into the same correlated, full-fidelity data model as cloud, SaaS, and identity, Defending Your AI with Helios AIDR delivers tangible outcomes:

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• Visibility – Clear, asset-level insight into how AI services and models are used, by whom, and with what data.
• Speed – Real time AI-specific detections and rich context accelerate triage and investigation.
• Accuracy – Signals are grounded in normalized telemetry and long-term history, reducing noise and improving explainability.
• Incident readiness – Teams can reconstruct end-to-end timelines for AI-related incidents and tune controls based on real evidence.
• Zero-Impact Breach Prevention – A single, agentless, and low-friction platform assures that, even when attackers reach your AI systems, rapid, informed response and attack containment help ensure they walk away with nothing of value.

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Cloud Defense Must Evolve into AI Defense

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The line between cloud security and AI security has vanished. AI now touches every layer of your cloud estate; from the applications your teams use, to the models your developers deploy, to the attacks your adversaries launch.

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That’s why Mitiga’s AIDR-native cloud detection and response platform was built from the ground up to unify Cloud, SaaS, AI, and Identity into one intelligent and AI-powered detection, investigation, and Zero-Impact Breach Prevention platform.

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Our mission: empower defenders to

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• ‍Defend with AI – by using AI as an active force multiplier in the SOC.
• Defend their AI – by safeguarding the AI infrastructure and services that power innovation.

Because in this new era, the question isn’t just how you’ll use AI to defend, but how you’ll defend the AI itself.

With Mitiga’s Helios AIDR, you can do both and ensure that even when attackers get in, they get nothing.

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Frequently Asked Questions About AI Security

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What are the main ways AI can be attacked?

AI systems face multiple threat vectors, including prompt injection attacks, model poisoning, data exfiltration through compromised tokens, jailbreaking attempts, and identity abuse through stolen credentials. These attacks target both AI infrastructure like AWS Bedrock and Azure OpenAI, as well as AI SaaS platforms like ChatGPT Enterprise and Claude.

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How do you detect attacks on AI infrastructure?

AI infrastructure like Claude or Bedrock and services like ChatGPT Enterprise introduce new attack vectors and visibility gaps that current security technologies can’t defend against. Detecting AI attacks requires continuous monitoring of token usage patterns, authentication flows, prompt behaviors, and cross-correlation with cloud and SaaS activity. Mitiga's AI-native CDR platform with Helios AIDR uses behavioral analytics and contextual telemetry to identify anomalous AI activity that indicates credential theft, data harvesting, or model abuse.

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What is prompt injection in AI security?

Prompt injection is an attack technique where adversaries craft malicious inputs to manipulate AI model behavior, bypass safety controls, or extract sensitive information. This can include tricking AI systems into revealing training data, executing unintended actions, or misclassifying malicious requests as legitimate.

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How do you secure ChatGPT Enterprise and other AI SaaS platforms?

Securing AI SaaS platforms requires monitoring user authentication, tracking token issuance and usage, analyzing prompt patterns for anomalies, implementing strong identity controls, and maintaining visibility across AI, cloud, and SaaS environments through unified detection and response platforms like Mitiga’s AI-native CDR platform with Helios AIDR.

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Keep Exploring the AIDR Revolution

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This blog is the second in my ongoing series exploring the evolution of AI Detection and Response (AIDR) and its transformative impact on modern, AI-native Cloud Detection and Response.

If you missed the first installment (Top 5 Best Practices for AI-Powered Cloud Detection and Incident Response) read it now to see how Mitiga helps security teams Defend with AI through automation, speed, and precision.

Stay tuned for the next post in this series, where I’ll dive into the last pillar of our Helios AIDR strategy: Defending from AI and combatting AI-centric and AI-scaled attacks.

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