Accelerated Innovation

Assessing and Identifying Sources of AI Security Risk

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Assessing and Identifying Sources of AI Security Risk

Description

This capability focuses on identifying, evaluating, and prioritizing AI-specific security risks that could arise during development, deployment, or use of GenAI solutions. It includes the adoption of risk assessment frameworks, collaboration with security teams, and integration of structured risk evaluations across the AI lifecycle.

Why it's Important

As GenAI systems become more embedded in critical workflows, the risks they introduce-from model vulnerabilities to data exposure-can be difficult to predict and even harder to manage. Without rigorous risk assessments, organizations are flying blind-unable to identify emerging threats or plan appropriate countermeasures. Embedding AI-specific risk identification processes early in the development cycle helps teams anticipate problems, design with safety in mind, and avoid costly delays or downstream incidents. Proactive risk management builds trust, strengthens compliance, and enables faster, safer AI scaling across the enterprise.

Why it's Challenging @ Scale

  • Unclear ownership for AI-specific risks: Many teams are unsure who is responsible for identifying and assessing security risks tied to GenAI development.
  • Lack of standardized frameworks: Traditional security assessments often don’t account for the unique threat models introduced by GenAI systems.
  • Difficulty integrating into agile workflows: Risk assessments are often seen as blockers, rather than enablers, making them difficult to embed into sprint-based development cycles.
  • Limited data to inform prioritization: Without structured monitoring or historical incident data, teams struggle to assess the likelihood and impact of different AI threats.
  • Fragmented collaboration between dev and security teams: Siloed responsibilities lead to blind spots and reactive responses instead of proactive risk management.

Complexity

High: Maturing this capability requires cross-functional alignment, integration with secure development workflows, and adoption of GenAI-specific risk assessment methods that evolve alongside emerging threats.

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Taking Action

Though most organizations begin their GenAI journey with significant knowledge gaps, there are targeted actions that can be taken to accelerate the process. Select your group’s current maturity, based on your assessment results, and act today.

The most important part of any journey is starting… To move from “Exploring” to “Experimenting”, focus on the following key actions:
  • Explore Key Concepts & Best Practices: Complete the Securing Your GenAI Solution workshop (2 hrs.) to understand foundational key concepts and explore applied best practices.
  • Introducing GenAI Threat Models and Security Posture
  • Understanding Attack Surfaces in GenAI Workflows
  • Establishing Basic Security Principles for LLMs
  • Identifying Security Stakeholders and Roles
  • Aligning Security with Compliance Requirements
  • Define Your Action Plan: Outline concrete, prioritized steps your organization will take to implement GenAI Strategy.
  • Align on your Current State and define your Target State
  • Create an actionable enablement plan
  • Define target timeline and measures of success
  • Deliver Quick Wins: Small, high-impact GenAI projects that can demonstrate tangible value in a short time frame.
  • Risk Triage Pilot: Identify and rank top GenAI risks across 1-2 high-profile projects.
  • Security-First Use Case Review: Evaluate an in-flight GenAI initiative with a security lens and document improvement areas.
  • Develop an AI Risk Assessment Checklist: Create a lightweight checklist that teams can use during solution design or testing.
To move from Experimentation to “Lifting-Off”, prioritize the following actions:
  • Complete one or more of our Deep Dive Courses: Begin exploring key concepts and best practices, including:
  • A Deep Dive into GenAi Solution Threat Modeling
  • A Deep Dive into Enterprise Access Control for GenAI Solutions
  • A Deep Dive into Preventing Prompt Injection Attacks
  • A Deep Dive into Preventing Insecure Output Handling
  • A Deep Dive into Preventing Data Poisoning
  • A Deep Dive into Preventing Denial of Service
  • A Deep Dive into Preventing GenAI Supply Chain Risks
  • A Deep Dive into Preventing Sensitive Information Disclosure
  • A Deep Dive into Preventing Insecure GenAI Solution Plugins
  • A Deep Dive into Preventing Excessive LLM Agency
  • A Deep Dive into Preventing LLM Overreliance
  • A Deep Dive into Preventing GenAI Model Theft
  • Nail It Before You Scale It: Assess and optimize your solution or process before adopting it at scale
  • Assess Your Proposed Solution or Process: Evaluate current methods for AI risk identification and determine where enhancements or standardization are needed.
  • Define in-scope Processes and Guardrails: Clarify which workflows require embedded risk assessment steps and define approval criteria.
  • Close any Data or Measurement Gaps: Establish reliable feedback loops, incident logging, and usage metrics to quantify AI-related risks over time.
  • Define Your Adoption & Scaling Plan: Create a structured roadmap for how GenAI solutions will be rolled out across teams, workflows, or business units
  • Define Your Phased Implementation Plan: Prioritize adoption of structured risk assessments in areas with the highest exposure or visibility.
  • Build Awareness and Finalize Enablers: Equip dev and security teams with playbooks, templates, and training to support responsible AI scaling.
  • Operationalize Your Comms Plan: Launch ongoing updates on GenAI risk posture, tool availability, and team responsibilities to drive adoption.
To move from Lifting-Off to “Accelerating”, prioritize the following actions:
  • Formalize Your Best Practices: Document and standardize what’s working to ensure consistent, scalable success across teams and use cases
  • Codify Your Risk Assessment Framework: Create a reusable framework tailored to GenAI, including categories, scoring, and workflows.
  • Standardize Assessment Templates: Build out consistent templates and checklists that help teams assess AI risks quickly and effectively.
  • Integrate Risk Reviews into Dev Lifecycle: Embed security risk checkpoints within sprint cycles, model reviews, and deployment gates.
  • Accelerate Your Adoption: Intensify efforts to embed GenAI across your organization by expanding use cases, increasing user engagement, and removing adoption barriers
  • Expand Assessment Coverage: Apply AI risk assessments beyond pilots-into full-stack systems, third-party tools, and production environments.
  • Enable Self-Service Tools: Provide product teams with user-friendly tools and dashboards to run and interpret their own AI risk assessments.
  • Conduct Targeted Security Audits: Periodically audit AI deployments to ensure risks are being correctly flagged, documented, and mitigated.
  • Celebrate Your Wins: Publicly acknowledge team accomplishments to build and sustain adoption momentum
  • Showcase High-Impact Risk Interventions: Highlight where early risk assessments prevented major security, reputational, or operational impacts.
  • Share Standardized Case Studies: Publish internal briefs showing how risk management improved GenAI project outcomes.
  • Recognize Cross-Functional Collaboration: Celebrate dev and security teams that partnered effectively to reduce AI risk.
The “Accelerating” stage represents “Target State” for many capabilities. “Breaking Away”, on the other hand, suggests that the specific Capability represents a clear competitive advantage for your business.
  • Streamline & Embed: Integrate GenAI into core workflows while eliminating friction points to make usage seamless and routine
  • Embed AI Risk Scoring into Tooling: Equip solution and security platforms with built-in risk scoring aligned to enterprise policies.
  • Provide Real-Time Risk Feedback: Offer developers in-line security alerts and risk recommendations as they build and deploy GenAI features.
  • Operationalize Risk-to-Value Mapping: Help teams balance innovation and risk by aligning assessment outputs to business impact metrics.
  • Leverage Automation: Use GenAI-powered tools and workflows to streamline repetitive tasks, enhance operational efficiency, and reduce manual effort
  • Automate Risk Reviews: Use AI to pre-assess risk profiles and flag missing safeguards in documentation or architecture.
  • Auto-Suggest Risk Controls: Provide system-generated recommendations to mitigate flagged risks based on historical resolution data.
  • Continuously Monitor Risk Trends: Use analytics to identify emerging threat patterns across GenAI deployments and update controls accordingly.
  • Evolve & Further Accelerate: Continuously refine GenAI strategies based on insights and outcomes, while expanding into more complex or high-impact use cases
  • Refresh Risk Taxonomies Regularly: Update your enterprise view of GenAI risk types as new tools, threats, and modalities emerge.
  • Benchmark Against Industry Standards: Compare your risk posture with peers to identify areas for differentiation and leadership.
  • Expand into Emerging Domains: Extend risk assessments into edge cases like multimodal AI, autonomous agents, or generative code.

Key "Watchouts"

As you take action you’ll want to avoid:

  • Overcomplicating risk processes: Long, manual assessments will frustrate teams and slow GenAI delivery.
  • Treating risk management as a checkbox: Superficial assessments lead to false confidence and overlooked threats.
  • Ignoring early development stages: Risks are easiest and cheapest to address during solution design-not post-deployment.
  • Underestimating new threat vectors: GenAI introduces risks that don’t exist in traditional software-teams must stay alert.
  • Failing to involve cross-functional teams: Risk blind spots emerge when product, security, and compliance teams operate in silos.

Targeted Benefits

While Assessing and Identifying Sources of AI Security Risk can be challenging, its benefits are clear and compelling, including:

  • Stronger solution integrity: Early risk identification reduces vulnerabilities in GenAI products.
  • Faster time-to-confidence: Teams build and ship with greater assurance when risks are visible and mitigated.
  • Lower incident costs: Preventing threats before they occur helps avoid rework, reputational damage, and regulatory penalties.
  • Increased trust with stakeholders: Well-managed risk builds confidence with internal leaders, regulators, and end users.
  • Clearer security ownership: Risk frameworks clarify who’s accountable and when action is needed.

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Eddie
Accelerated Innovation

Hi, I'm Eddie 👋

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