One Article Review
| Source |  ProofPoint |
|---|---|
| Identifiant | 8642164 |
| Date de publication | 2025-01-24 05:28:30 (vue: 2025-01-24 18:07:59) |
| Titre | Unlocking the Value of AI: Safe AI Adoption for Cybersecurity Professionals |
| Texte | As a cybersecurity professional or CISO, you likely find yourself in a rapidly evolving landscape where the adoption of AI is both a game changer and a challenge. In a recent webinar, I had an opportunity to delve into how organizations can align AI adoption with business objectives while safeguarding security and brand integrity. Michelle Drolet, CEO of Towerwall, Inc., hosted the discussion. And Diana Kelley, CISO at Protect AI, participated with me. What follows are some key takeaways. I believe every CISO and cybersecurity professionals should consider them when they are integrating AI into their organization. Start with gaining visibility into AI usage The first and most critical step is gaining visibility into how AI is being used across your organization. Whether it\'s generative AI tools like ChatGPT or custom predictive models, it\'s essential to understand where and how these technologies are deployed. After all, you cannot protect what you cannot see. Start by identifying all large language models (LLMs) and the AI tools that are being used. Then map out the data flows that are associated with them. Balance innovation with guardrails AI adoption is inevitable. The “hammer approach” of banning its use outright rarely works. Instead, create tailored policies that balance innovation with security. For instance: Define policies that specify what types of data can interact with AI tools Implement enforcement mechanisms to prevent sensitive data from being shared inadvertently These measures empower employees to use AI\'s capabilities while ensuring that robust security protocols are maintained. Educate your employees One of the biggest challenges in AI adoption is ensuring that employees understand the risks and responsibilities that are involved. Traditional security awareness programs that focus on phishing or malware need to evolve to include AI-specific training. Employees must be equipped to: Recognize the risks of sharing sensitive data with AI Create clear policies for complex techniques like data anonymization to prevent inadvertent exposure of sensitive data Appreciate why it\'s important to follow organizational policies Conduct proactive threat modeling AI introduces unique risks, such as accidental data leakage. Another risk is “confused pilot” attacks where AI systems inadvertently expose sensitive data. Conduct thorough threat modeling for each AI use case: Map out architecture and data flows Identify potential vulnerabilities in training data, prompts and responses Implement scanning and monitoring tools to observe interactions with AI systems Use modern tools like DSPM Data Security Posture Management (DSPM) is an invaluable framework for securing AI. By providing visibility into data types, access patterns and risk exposure, DSPM enables organizations to: Identify sensitive data being used for AI training or inference Monitor and control who has access to critical data Ensure compliance with data governance policies Test before you deploy AI is nondeterministic by nature. This means that its behavior can vary unpredictably. Before deploying AI tools, conduct rigorous testing: Red team your AI systems to uncover potential vulnerabilities Use AI-specific testing tools to simulate real-world scenarios Establish observability layers to monitor AI interactions post-deployment Collaborate across departments Effective AI security requires cross-departmental collaboration. Engage teams from marketing, finance, compliance and beyond to: Understand their AI use cases Identify risks that are specific to their workflows Implement tailored controls that support their objectives while keeping the organization safe Final thoughts By focusing on visibility, education and proactive security measures, we can harness AI\'s potential while minimizing risks. If there\'s one piece of advice that I\'d leave you with, it\'s this: Don\'t wait for incidents to highlight the gaps in your AI strategy. Take the first step now by auditing |
| Notes | ★★ |
| Envoyé | Oui |
| Condensat | participated access accidental across adoption adoption: advancements advice after ai: align all and diana anonymization another appreciate approach” architecture are associated at protect attacks auditing awareness balance banning before behavior being believe beyond biggest both brand building business can cannot capabilities case: cases ceo challenge challenges changer chatgpt check ciso clear collaborate collaboration complex compliance conduct consider control controls create critical cross culture custom cybersecurity data define delve departmental departments deploy deployed deploying deployment discussion don drolet dspm each educate education effective employees empower enables enforcement engage ensure ensuring equipped essential establish every evolve evolving explore our expose exposure final finance find first flows focus focusing follow follows foundation framework from gaining game gaps generative governance guardrails had harness has highlight hosted how identify identifying implement important inadvertent inadvertently inc incidents include inevitable inference innovation insights instance: instead integrating integrity interact interactions introduces invaluable involved its keeping kelley key landscape language large latest layers leakage leave like likely llms maintained malware management map marketing means measures mechanisms michelle minimizing modeling models modern monitor monitoring more most must nature need nondeterministic now objectives observability observe of towerwall one opportunity organization organizational organizations out outright page patterns phishing piece pilot” policies post posture potential predictive prevent proactive professional professionals programs prompts protect protecting protocols providing rapidly rarely real recent recognize red requires responses responsibilities rigorous risk risks robust safe safeguarding scanning scenarios secure securing security see sensitive shared sharing should simulate some specific specify start step strategy such support systems tailored take takeaways team teams techniques technologies test testing testing: them then there these this: thorough thoughts threat to: tools traditional training types uncover understand unique unlocking unpredictably usage use used value vary visibility vulnerabilities wait watch web webinar webinar: what when where whether who why workflows works world your yourself “confused “hammer “safe |
| Tags | Malware Tool Vulnerability Threat Legislation |
| Stories | ChatGPT |
| Move | 
|
L'article ne semble pas avoir été repris aprés sa publication.
L'article ne semble pas avoir été repris sur un précédent.