Latest surveys reveal that 67% of organizations are increasing their investment in Generative AI (GenAI) initiatives. GenAI adoption is driven by the strong productivity gains observed in enterprises. It is pushing the boundaries with increased efficiency, reduced costs, and accelerated innovation.
However, as organizations deeply embed AI into their core functions, seeing the enhanced agility and speed it offers, ensuring a robust AI security posture becomes imperative. Here, AI Security Posture Management (AI SPM) is crucial in preventing the new category of risks that AI introduces.
Let’s discuss and learn more about AI SPM, the myriad of benefits it offers, the new category of risks it helps enterprises overcome, and its core capabilities.
What is AI SPM?
AI SPM stands for AI Security Posture Management. In its simplest form, AI SPM represents a comprehensive approach to ensuring the security and integrity of AI systems throughout their lifecycle. This entails a set of strategies, tools, and frameworks that work in tandem to monitor, evaluate, and mitigate the risks unique to AI.
In contrast, security teams rely on cloud security posture management (CSPM) tools to evaluate and mitigate the risks associated with cloud infrastructure. These risks may include cloud misconfigurations, policy violations, and insecure access controls, among other potential issues. Similarly, on the data front, data security posture management (DSPM) tools provide detailed insights into data visibility, associated risks such as unintended access or sensitive data exposure, and best practices for mitigation.
However, AI is a relatively new playground for cybersecurity professionals. A new stream of capabilities is required to protect AI models, data pipelines, and resources. AI SPM fills this critical void by identifying and mitigating risks associated with AI models, Agents, and Copilots, ensuring the safe and responsible use of AI. The tool integrates seamlessly with other security tech stacks to enable holistic data and AI security, governance, and compliance.
Why AI SPM is Important
AI adoption has gained exponential momentum over the past few years. McKinsey cites in its latest report that 78% of organizations leverage AI in at least one business function. The percentage increased from 72%, as surveyed in early 2024, to 55% in 2023.
However, as AI emerges as a critical component of key business operations, ensuring its safe and responsible use has also become challenging. McKinsey cites in another report that 91% of organizations don’t feel truly prepared for the safe and responsible use of AI.
This makes a compelling case for why organizations must adopt AI SPM as a key enabler of accelerated AI adoption. Here are some equally compelling reasons why AI SPM must be an essential part of the cybersecurity technology stack.
Comprehensive Visibility
Little to no visibility into what AI systems or applications operate across an enterprise environment can put it at risk. Unsanctioned AI, compliance violations, and the exposure of sensitive data can put enterprises at risk for security, legal, and reputational issues. AI SPM helps enterprises build a comprehensive inventory of all AI models operating across multi-cloud environments.
AI visibility helps businesses achieve multiple objectives. For instance, early detection of risks, such as sensitive data exposure to AI models, can help organizations prevent security mishaps. Organizations can immediately detect AI systems operating out of jurisdictions, preventing cross-border data transfer compliance violations. Similarly, organizations can detect redundant or unnecessary AI tools, which can ultimately help optimize cost and efficiency.
Watch Spotlight Talk: From Shadow AI to Secure GenAI
AI-Specific Risks Monitoring & Protection
Generative AI (GenAI) has introduced a series of unprecedented risks. For instance, the Open Web Application Security Project (OWASP) lists the top 10 risks associated with large language models (LLMs). At the same time, MITRE ATLAS™ (Adversarial Threat Landscape for Artificial-Intelligence Systems) has also identified over 60 different attacks targeting AI systems. AI SPM can effectively detect, monitor, and protect against such risks with automated policies, controls, and orchestration.
AI risk detection and mitigation enable enterprises to ensure the responsible use of AI. As their LLMs are protected against risks like prompt injection or biased responses, enterprises can ensure that their customers can confidently rely on AI applications.
Holistic Security Strategy
CSPM, DSPM, and AI SPM are fundamentally distinct technologies, each with unique scopes, capabilities, and objectives. However, under the broader lens, all these technologies converge to offer a holistic approach to enterprise cybersecurity.
For instance, CSPM can effectively identify and mitigate cloud misconfigurations, but it fails to provide risk insights into AI models. DSPM can provide a comprehensive view of all data, its lineage, and associated risks. Similarly, AI SPM can help organizations establish security controls around AI models, but not around the cloud resources or data processing pipelines.
Regulatory Compliance
The regulatory landscape has undergone significant transformation over the past few years. AI-specific laws and frameworks have now been incorporated into the already complex web of regulations. The EU AI Act, the Brazilian AI Act, and the NIST AI RMF are among the frameworks that require businesses to consider the explainability of AI output, the traceability of models, risk awareness, and the responsible use of AI, among other key aspects.
AI SPM helps businesses comply with AI laws, demonstrating adherence to ethical practices and encouraging the responsible use of AI. Automated compliance checks, assessment controls, and regulatory intelligence are among the key components of AI SPM that enable compliance.
The Silent Threat Landscape of AI
As discussed earlier, organizations worldwide are competing to integrate AI into their business operations. However, hidden elements, such as the presence of unsanctioned AIs, ethical and regulatory violations, and increasing AI-specific risks, prevent organizations from accelerating their efforts to adopt AI responsibly. Let’s take a quick look at the cascading risks that could affect organizations when AI is introduced without robust governance, policies, and controls.
Shadow AI Lurks in the Dark
Shadow IT has been a persistent challenge for security and governance teams for decades. However, it can be effectively managed with the right policies. Shadow AI, on the other hand, is a different beast. It is much more pervasive than its counterpart and poses more concerning legal repercussions, security risks, and governance challenges. Without eyes on unsanctioned AIs, organizations are susceptible to risks like prompt injection, model poisoning, model theft, unintended access, etc.
Higher Risks Equate to Higher Consequences
The response of AI models depends heavily on the quality of the data used to train or fine-tune the large language model (LLM). Without proper data security and governance controls, AI models are at risk of producing biased, discriminatory, and hallucinatory responses. Consequently, this opens the door to regulatory violations and customer distrust.
Unsecured Models are Susceptible to OWASP Top 10 for LLMs
A lack of appropriate AI security controls can leave LLMs vulnerable to the myriad of risks, as highlighted by the Open Worldwide Application Security Project (OWASP). The OWASP top 10 for LLM is a list of risks that are common in AI systems and applications. For instance, threat actors can manipulate the intended behavior of an LLM with a prompt injection attack. The LLM output can be skewed by tampering with the data used to train the model, a phenomenon known as training data poisoning.
The OWASP top 10 for LLM further highlights that LLMs are vulnerable to risks at every level and interaction. Unguarded prompts, unfiltered responses, and insecure retrievals can threaten the safety of users. Responsible AI requires organizations to establish proper guardrails at every interaction to ensure the safe use of AI.
6 Key Capabilities of AI SPM
As discussed earlier, the road to the safe and responsible use of AI is laden with overwhelming challenges and risks. Organizations require a strategic framework to overcome those hurdles and protect AI initiatives across their lifecycle. The following are some best practices frameworks, designed by Securiti.ai, that give a head start to enterprises looking to protect and accelerate AI adoption.
1. AI Models & Agents Discovery
A well-governed AI SPM begins with comprehensive visibility of all AI models, including sanctioned and unsanctioned AI, operating across public clouds and SaaS applications. This means that security teams must maintain a comprehensive inventory of AI models, including their metadata that details ownership, data usage, entitlements, configurations, and other relevant information. This enables organizations to assess risks, impacts, and compliance needs effectively.
2. AI Model Risks Assessment
The second most important step is to assess, classify, and rate AI models based on their risk ratings. This step is critical for ensuring compliance with global AI regulations, such as the EU AI Act, which requires the classification of AI systems and, based on that, enforces certain obligations and restrictions on their use. The ratings should encompass key ethical, governance, and security aspects, including model toxicity, efficiency, bias, and hallucinatory risks.
3. Data+ AI Interaction Understanding
AI transparency and explainability are also critical to the safe use of AI. Apart from compliance obligations, transparency and explainability further help teams understand the system dependencies and potential points of failure, enabling them to fine-tune their AI performance and ensure efficiency. To achieve these objectives, organizations must develop a comprehensive map of AI and its interactions with data sources, processing activities, potential risks, and regulatory obligations.
4. Safe Ingestion of Data (Sanitization & Entitlements)
Once security teams have insights into the data and AI, they must implement in-line data and AI controls. Data needs to be protected before AI models ingest it for training, fine-tuning, or inference. Scan and classify all the data across on-premise, SaaS, and cloud environments. Strict data sanitization and access controls must be enforced to protect sensitive data flowing into LLMs or generated as output. Data sanitization controls may include redaction, anonymization, or masking sensitive data on the go.
Watch Spotlight Talk: Evolution of Data Controls in the Era of Generative AI
5. LLM Firewalls
GenAI pipelines are vulnerable to a myriad of attacks at various points of interaction, including prompts, responses, and retrievals. To prevent risks such as biased responses, sensitive data leakage, and prompt injection attacks, LLM firewalls should be placed at every interaction point. For instance, a prompt firewall filters out unwanted or malicious prompts that could end up affecting the behavior of the model or sensitive data leak. The retrieval firewall ensures the relevancy and accuracy of the data while also blocking sensitive data exposure, poisoned or malicious data, and indirect prompt injection attacks. Similarly, the response firewall helps ensure secure and appropriate output content.
6. Compliance Management
AI systems and operations must be aligned with industry regulations and best practices frameworks, like the EU AI Act and the NIST AI RMF. Map data and AI processing with a regulatory knowledge base and run automated assessment checks. Common tests and controls give organizations the ability to gain a bird’s-eye view of the compliance posture, detect compliance risks, and remediate them proactively.
Accelerate AI Adoption with a Methodical AI SPM Framework
Ungoverned or uncontrolled AI can lead to serious risks down the line for enterprises. To accelerate AI adoption and reap tremendous business value out of AI initiatives, organizations must now add AI SPM to their existing technology stack. Moreover, the AI SPM framework should be methodical and layered, securing AI at every level of its lifecycle, i.e., from creation to production. Robust policies and controls must be placed at every event of the AI or LLM interaction, making sure that the risks are managed and the expanded attack surface is well protected.
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